feat(cnn): publish eight-class weights, loader, and notebook

Publish the selected seed-17 ordered poem CNN as the root default and cnn-eight-class package. Include the self-contained Transformers loader, loading script, updated notebook, evaluation, and source revision. Preserve all original cnn/ and byt5/ packages and their hf_model.py loader.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cnn-eight-class/confusion_matrix.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,194 +1,60 @@
 ---
-language:
-  - te
+language: [te]
 license: other
 license_name: chandassu-citation
-license_link: https://huggingface.co/sprx7767/chandassu-recognition/blob/main/cnn/LICENSE
+license_link: LICENSE
 library_name: transformers
 pipeline_tag: text-classification
-tags:
-  - telugu
-  - poetry
-  - poetic-meter
-  - cnn
-  - byt5
-  - safetensors
+tags: [telugu, poetry, poetic-meter, cnn, eight-class, safetensors]
 ---
 
-# Chandassu Recognition
+# Chandassu Recognition — eight-class poem CNN
 
 **Author: Pranav Surampudi**
 
-Chandassu Recognition identifies four Telugu poetic meters:
-**ఉత్పలమాల · చంపకమాల · మత్తేభము · శార్దూలం**.
-
-The current CNN was trained on **NVIDIA GB10 (GX10)** using the expanded v2 corpus.
-The ByT5 package remains the original **NVIDIA H100 NVL** model on v1 data.
-This repository provides a character CNN and a ByT5 encoder classifier.
-Use the CNN when you need a smaller model.
-
-## Model packages
-
-| Model | Directory | Parameters | Seed | Selected epoch |
-|---|---|---:|---:|---:|
-| Character CNN | [cnn](https://huggingface.co/sprx7767/chandassu-recognition/tree/main/cnn) | 68,484 | 73 | 58 |
-| ByT5 encoder | [byt5](https://huggingface.co/sprx7767/chandassu-recognition/tree/main/byt5) | 217,663,364 | 17 | 17 |
-
-Each directory contains SafeTensors weights, configuration, input encoding, calibration settings, and model metadata.
-Use `AutoTokenizer` and `AutoModelForSequenceClassification` to load either model in a Python environment.
-You can also use the project loader and web interface.
-
-[Project code and instructions](https://github.com/Pranav-20186017/chandassu_recognition)
-
-## Architectures
-
-The CNN encodes 93 characters, plus padding and unknown tokens, with 64-dimensional embeddings.
-Three convolution branches use kernel sizes 3, 5, and 7.
-Each branch applies ReLU and masked max pooling to produce 64 features.
-A linear layer maps the combined 192 features to four class logits.
-
-The ByT5 model uses the encoder from [google/byt5-small](https://huggingface.co/google/byt5-small).
-The base revision is `68377bdc18a2ffec8a0533fef03b1c513a4dd49d`.
-The encoder has 12 layers and a hidden width of 1,472.
-Masked mean pooling and a linear classification layer produce four class logits.
-
-Both models classify individual lines.
-Temperature calibration adjusts the line probabilities.
-The mean of four line probability vectors gives the poem probabilities.
-The class with the highest probability is the poem prediction.
-
-[Architecture diagrams](https://github.com/Pranav-20186017/chandassu_recognition#model-architectures)
-
-## Data and sources
-
-The poems come from [AndhraBharati](https://andhrabharati.com/) and [Telugu Wikisource](https://te.wikisource.org/).
-The current CNN uses **13,295 complete poems**, **53,180 lines**, and **70 retained works**
-from v2, which already includes v1. ByT5 continues to use v1: 11,518 poems, 46,072 lines,
-and 36 retained works. The CNN update does not retrain or replace ByT5.
-
-| Use | CNN v2 poems / lines | ByT5 v1 poems / lines |
-|---|---:|---:|
-| Weight training | 7,575 / 30,300 | 6,517 / 26,068 |
-| Temperature calibration | 908 / 3,632 | 908 / 3,632 |
-| Validation | 1,859 / 7,436 | 1,859 / 7,436 |
-| Holdout test | 2,953 / 11,812 | 2,234 / 8,936 |
-| Total | 13,295 / 53,180 | 11,518 / 46,072 |
-
-All original v1 split assignments are preserved. Of 1,777 new poems from 34 works,
-1,058 poems from 27 works join fitting and 719 poems from seven works remain test.
-The new-work test allocation was frozen before training. One CNN test file combines
-the original 2,234 test poems and the 719 new-work test poems. Validation is unchanged.
-The 908 calibration poems are inside canonical train but never update model weights.
-
-Validation poem Macro F1 selects each checkpoint. Selected-checkpoint validation F1,
-then unweighted validation line cross-entropy, selects the released single CNN across
-seeds. Temperature is fitted separately for each model after checkpoint selection.
-Test data are excluded from fitting, vocabulary construction, selection, and calibration.
-All four ordered lines, work/edition families, and duplicate-linked works stay together.
-Authors may overlap. Source annotations and rule provenance are retained; these are
-not independently verified expert gold labels. Both CNN test cohorts were evaluated
-on earlier models: this is a fixed benchmark, not a new blind test or unseen-author study.
-
-The [v2 work catalogue](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/v2/WORKS.md)
-records the new works and their fitting/test assignments. The complete machine-readable
-catalogue includes 80 collected works, of which 70 retain supervised four-class poems.
-The [v1 catalogue](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/v1/WORKS.md)
-retains the original collection, including non-target and excluded material.
-
-## Training hyperparameters
-
-| Hyperparameter | CNN | ByT5 |
-|---|---:|---:|
-| Initial learning rate | 0.0001 | 0.00003 |
-| Microbatch size, poems | 16 | 8 |
-| Effective batch size, poems | 16 | 16 |
-| Evaluation batch size, poems | 16 | 8 |
-| Maximum epochs | 60 | 32 |
-| Minimum epochs | 24 | 8 |
-| Early stopping: epochs without improvement | 12 | 8 |
-| Input limit per line | 256 characters | 1,024 byte tokens, including the end token |
-| Gradient checkpointing | Off | Off |
-
-Both models use AdamW, weight decay 0.01, dropout 0.2, and gradient clipping 1.0.
-ReduceLROnPlateau uses validation line cross-entropy loss without class weights.
-The scheduler has patience 4, multiplier 0.5, and minimum learning rate 0.000001.
-The H100 study selected initial CNN LR 0.0001; the expanded GX10 run reuses it for
-seeds 17, 42, and 73 without another rate search or nested-fold study. All three CNNs
-share the same frozen partitions and architecture. They start from fresh weights.
-This repository contains the validation-selected CNN seed 73 and unchanged ByT5 seed 17.
-
-The exported calibration temperatures are **1.401754510250526** for CNN and **1.1801772437687996** for ByT5.
-CNN runtime: Python 3.12.3, PyTorch 2.10.0a0+b558c986e8.nv25.11, CUDA 13.0, NVIDIA GB10, BF16.
-Its training checkout was clean at `f7939ccac30d14bab929ed7d9c91aa202c5a9c80`.
-
-## Current CNN results
-
-| CNN seed | Selected epoch | Validation poem Macro F1 | Validation line CE before temperature | Temperature |
-|---|---:|---:|---:|---:|
-| 17 | 54 | 0.998596 | 0.031031 | 1.339218 |
-| 42 | 60 | 0.998596 | 0.031057 | 1.396669 |
-| 73 (released) | 58 | 0.998596 | 0.028452 | 1.401755 |
-
-Seed 73 wins the validation F1 tie through lower validation CE. Test results never
-select the seed. Every seed completed 60 epochs, followed by separate temperature
-calibration on the 908 calibration poems. Validation scores are checkpoint-selected.
-
-| Released CNN seed 73 benchmark | Poems | Correct | Poem Macro F1 | Line Macro F1 |
-|---|---:|---:|---:|---:|
-| Original v1 test | 2,234 | 2,234 | 1.000000 | 0.993992 |
-| Frozen new-work test | 719 | 716 | 0.996042 | 0.991346 |
-| Combined test | 2,953 | 2,950 | 0.999084 | 0.993420 |
-
-All three replicas and their predetermined equal-probability ensemble have the same
-combined-test poem score and the same three errors. Matched CPU inference with the
-previously released v1 CNN seed 17 produces the same poem predictions on these 2,953
-poems. There is **no measured poem-level gain** from this update. Combined-test line
-Macro F1 changes from 0.992608 (old CNN) to 0.993420 (new CNN); hardware/runtime,
-vocabulary, seed, and data differ, so this does not isolate the effect of corpus growth.
-The local three-seed ensemble line Macro F1 is 0.993592; it offers no poem-level gain.
-Ensemble membership and equal weights were fixed before evaluation.
-
-The returned checkpoints and native SafeTensors were compared tensor-for-tensor;
-file manifests, code identity, all split fingerprints, calibration assignments and
-checkpoint selection were verified. Mac CPU inference reproduces the test confusion
-matrices and F1 scores. The exported Transformers package preserves the selected weights.
-See the CNN directory's selection, test and verification reports.
-
-## Original H100 study (v1), retained for comparison
-
-| Original v1 measure | Original CNN | Unchanged ByT5 |
-|---|---:|---:|
-| Cross-validation poem Macro F1, mean across three seeds | 0.997130 | 0.997137 |
-| Correct historical test poems, seed 17 | 2,234 / 2,234 | 2,233 / 2,234 |
-| Historical test poem Macro F1, seed 17 | 1.000000 | 0.999619 |
-
-These original study scores do not describe a repeated cross-validation experiment
-on v2. The historical test had been examined during earlier project work. Labels
-combine source annotations and rule-based checks; experts have not verified every label.
-[Original study evidence](https://github.com/Pranav-20186017/chandassu_recognition/tree/main/results/v1)
-
-## Run inference
-
-### Python
-
-Install the tested dependencies in your Python environment:
+The default model is the new **eight-class ordered poem CNN**, trained from scratch
+on the original four vruttas plus ఆటవెలది, తేటగీతి, కందము and సీసము.
+**Test poem Macro F1: 94.84% · accuracy: 97.90%** on 5,282 held-out poems.
+Ataveladi is the weakest class (69.34% F1); inspect the class-specific results below.
+This is a raw-text classifier with uncalibrated scores, not a formal prosody verifier.
+
+## Packages and loader
+
+| Package | Location | Output | Status |
+|---|---|---|---|
+| New eight-class CNN, seed 17 | Root, or `cnn-eight-class/` | Eight raw logits per complete poem | Current default |
+| Original four-class CNN | `cnn/` | Four raw logits per line | Preserved |
+| Original ByT5 encoder | `byt5/` | Four raw logits per line | Preserved; no retraining |
+
+`hf_poem.py` supplies the self-contained new tokenizer, config and model classes.
+`hf_model.py` and all original `cnn/` and `byt5/` files remain unchanged. The
+[original four-class card](legacy-four-class/README.md) retains their detailed
+architecture, calibration, results and usage. Do not mix their label spaces in an ensemble.
+
+[Open the updated notebook](notebook.ipynb) or use
+[Colab's Hugging Face URL](https://colab.research.google.com/github/Pranav-20186017/chandassu_recognition/blob/main/notebook.ipynb).
+It defaults to the new model, pins one resolved revision for all loaders, and
+retains an optional original-model example. Published execution outputs are cleared.
+
+## Python loading
+
+Tested with Python 3.12, PyTorch 2.8.0, Transformers 4.57.6, SafeTensors 0.8.0 and
+huggingface-hub 0.36.2. The exported loader requires only PyTorch, Transformers and
+the standard library; no project checkout, ByT5 download or login is needed.
 
 ```bash
-pip install "torch==2.8.0" "transformers==4.57.6" "safetensors==0.8.0"
+pip install "torch==2.8.0" "transformers==4.57.6" "safetensors==0.8.0" "huggingface-hub==0.36.2"
 ```
 
-Use the Transformers Auto interfaces to load the tokenizer and classifier.
-The tokenizer applies the same text normalization and input encoding used in training.
-The model returns four raw logits for each line.
-Apply the saved calibration temperature, then average line probabilities to classify a poem.
-
 ```python
 import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from huggingface_hub import HfApi
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 repo = "sprx7767/chandassu-recognition"
-options = {"subfolder": "cnn", "trust_remote_code": True}
-# Change "cnn" to "byt5" to load the ByT5 encoder classifier.
+revision = HfApi().model_info(repo, token=False).sha  # resolve once; keep this commit
+options = {"revision": revision, "trust_remote_code": True, "token": False}
+# Optional: options["subfolder"] = "cnn-eight-class"
 tokenizer = AutoTokenizer.from_pretrained(repo, **options)
 model = AutoModelForSequenceClassification.from_pretrained(repo, **options).eval()
 
@@ -196,101 +62,114 @@ poem = """శ్రీ కైవల్య పదంబుఁ జేరుటక
 క్షైకారంభకు, భక్త పాలన కళా సంరంభకున్, దానవో
 ద్రేకస్తంభకుఁ, గేళి లోల విలసద్దృగ్జాల సంభూత నా
 నా కంజాత భవాండ కుంభకు, మహానందాంగనాడింభకున్."""
-lines = [line.strip() for line in poem.splitlines() if line.strip()]
-if len(lines) not in (1, 4):
-    raise ValueError("Supply one line or a four-line poem")
-inputs = tokenizer(lines, padding=True, return_tensors="pt")
+inputs = tokenizer(poem, padding=True, return_tensors="pt")
 with torch.inference_mode():
-    logits = model(**inputs).logits
-    line_scores = (logits.double() / model.config.calibration_temperature).softmax(dim=-1)
-    scores = line_scores.mean(dim=0)
-
-winner = scores.argmax().item()
-print(model.config.id2label[winner], f"{scores[winner].item():.2%}")
-for index, score in enumerate(scores):
-    print(model.config.id2label[index], f"{score.item():.2%}")
+    scores = model(**inputs).logits.double().softmax(-1)[0]
+print(model.config.id2label[int(scores.argmax())])
+for i in scores.argsort(descending=True).tolist():
+    print(model.config.id2label[i], f"{float(scores[i]):.2%}")
 ```
 
-`AutoModel.from_pretrained(repo, **options)` also loads this classifier.
-`trust_remote_code=True` loads the Python classes supplied in the model repository.
-For a fixed model version, add `"revision": "<full commit ID>"` to `options`.
-The example was tested with Python 3.12 and the dependency versions listed above.
-Input limits are 256 characters per line for CNN and 1,024 byte tokens, including the end token, for ByT5.
-The tokenizer rejects overlong input and does not truncate lines.
-
-Class probabilities are calibrated scores, not statistical confidence intervals.
-A standard text-classification pipeline applies its own softmax to line logits.
-Use the calculation above for calibrated poem scores.
-Clone this project when you need the web interface, ensembles, or training.
-
-### Web interface
-
-Install the project and its dependencies:
+`trust_remote_code=True` loads the inspectable `hf_poem.py` from the pinned commit.
+Use `AutoModel` or `AutoModelForSequenceClassification`; both load the same classifier.
+The [loading script](load_hf_model.py) reads a complete UTF-8 poem text file:
 
 ```bash
-git clone https://github.com/Pranav-20186017/chandassu_recognition.git
-cd chandassu_recognition
-uv sync --locked --extra cpu
-source .venv/bin/activate
+python load_hf_model.py --poem poem.txt --revision FULL_HF_COMMIT
 ```
 
-Download and serve the CNN:
+### Whole-poem input contract
+
+- Pass one complete newline-separated poem string, or a list of complete poem strings.
+- Four full ordered pādas are required for the general eight-class prediction.
+- Eight Seesamu body half-lines are paired into four full body pādas. Twelve lines
+  mean eight body half-lines plus four closing geeti lines; the closing geeti is excluded.
+- An eight-line input means body half-lines, not four full body pādas plus four geeti lines.
+- Maximum 512 characters per full pāda; overlong inputs raise rather than truncate.
+- Token tensors have shape `[batch, 4, tokens]`; logits have shape `[batch, 8]`.
+- Scores are uncalibrated. There are no independent per-line scores or line probabilities to average.
+- A standard line-level text-classification pipeline is unsupported; use the Auto interfaces above.
+
+The original four-class models still support one line or a four-line poem with their
+original tokenizers and temperature-calibrated line averaging; see the preserved card.
+
+## Architecture and training
+
+The 266,056-parameter CNN shares 64-wide character embeddings and convolution
+branches (kernels 3, 5, 7) across all four ordered pādas. Each branch uses masked
+max and mean pooling. The four vectors are concatenated in order and passed to a
+128-unit classification head. The saved weights are unchanged by export.
+No source IDs, labels, rule scores, closing geeti, or intermediate prosody features
+enter the model. The vocabulary is fitted only on optimization data.
+
+Fresh seed 17; AdamW at 0.001; weight decay 0.01; batch 32 poems; dropout 0.2;
+gradient clip 1; square-root inverse-frequency class weights from fit only.
+Up to 40 epochs, minimum 16, early-stopping patience 8. ReduceLROnPlateau monitors
+unweighted validation poem CE (patience 3, factor 0.5, floor 1e-6).
+Validation **poem Macro F1** selects checkpoints, with unweighted poem CE for ties.
+Epoch **11** was selected after **19** completed epochs; validation Macro F1 was
+**86.14%**. Training used CPU/fp32; ByT5 was parked. The 1,558 reserved calibration
+poems were unused, so scores must not be described as calibrated confidence.
+
+## Data and results
+
+Data v3 contains **23,029 complete poems from 99 works**. It includes every v2
+poem with its existing role preserved. Original target classes retain IDs 0–3;
+new classes use IDs 4–7 in the order listed below.
+
+| Class | Fit | Validation | Reserved | Test | Test F1 |
+|---|---:|---:|---:|---:|---:|
+| ఉత్పలమాల | 2,215 | 585 | 324 | 996 | 99.75% |
+| చంపకమాల | 2,501 | 648 | 276 | 759 | 99.67% |
+| మత్తేభము | 1,789 | 423 | 194 | 733 | 99.80% |
+| శార్దూలం | 1,070 | 203 | 114 | 465 | 99.78% |
+| ఆటవెలది | 289 | 1,092 | 75 | 152 | 69.34% |
+| తేటగీతి | 943 | 209 | 10 | 640 | 92.27% |
+| కందము | 2,571 | 721 | 435 | 1,071 | 99.63% |
+| సీసము body | 680 | 250 | 130 | 466 | 98.50% |
+| **Total** | **12,058** | **4,131** | **1,558** | **5,282** | **Macro F1 94.84%** |
+
+On the same 2,953 original-label v2 test poems, four-label retention Macro F1 is
+99.81% (all eight prediction classes still available), versus 99.91% for the
+installed original four-class CNN. This diagnostic comparison does not establish
+future-source equivalence, and the old classifier cannot recognize the new labels.
+
+Whole poems, works, edition families and exact-duplicate-linked works stay together.
+Seesamu closures are included in isolation checks even though they are not features.
+The exact-duplicate screen is not proof against fuzzy duplicates or paraphrases.
+Authors can overlap across roles. Vemana dominates Ataveladi validation, while its
+fit set has only 289 poems across seven works. Source annotations are not expert gold;
+the test includes the previously inspected v2 benchmark. It is not wholly blind or
+an unseen-author evaluation. No test metric selected the checkpoint, recipe or split.
+
+One pre-test trial was stopped to exclude 23 new records with identifiable source
+corruption; it is kept locally as discarded audit evidence. The reported fit uses
+the same fixed seed and recipe, with no test-informed tuning.
+
+[Complete reports and figures](https://github.com/Pranav-20186017/chandassu_recognition/tree/main/results/v3) ·
+[Dataset](https://github.com/Pranav-20186017/chandassu_recognition/tree/main/data/v3) ·
+[Sources and rules](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/SOURCES.md) ·
+[All works](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/v3/WORKS.md)
+
+## Existing web interface
+
+Clone the [project](https://github.com/Pranav-20186017/chandassu_recognition) and install
+with `uv sync --locked --extra cpu`. Download and serve the new package:
 
 ```bash
-hf download sprx7767/chandassu-recognition \
-  --include 'cnn/*' --local-dir models/hf
-
-python inference.py --model_dir=models/hf/cnn --strategy=single --port=8765
+hf download sprx7767/chandassu-recognition --include 'cnn-eight-class/*' --local-dir models/hf
+python inference.py --model_dir models/hf/cnn-eight-class
 ```
 
-For ByT5, use these commands instead:
-
-```bash
-hf download sprx7767/chandassu-recognition \
-  --include 'byt5/*' --local-dir models/hf
-
-python inference.py --model_dir=models/hf/byt5 --strategy=single --port=8765
-```
-
-Open [http://127.0.0.1:8765](http://127.0.0.1:8765).
-Paste a single line or a four-line poem.
-The interface displays the meter, class probabilities, and line predictions.
-It supports light and dark themes.
-The API endpoint is `POST /api/predict` with a JSON request containing `text`.
-
-You can also train additional models with different seeds and hyperparameters.
-Use `--model_dirs` and `--strategy=ensemble` to load them together.
-Each ensemble requires distinct seeds from one model family.
-
-## Limitations
-
-Use these models to classify the four listed meters.
-They can assign high probabilities to poems from other meters.
-A high probability does not establish that an input belongs to a supported class.
-Class probabilities and score ranges are not statistical confidence intervals.
-The models do not produce syllable divisions, Guru/Laghu patterns, or gaṇa sequences.
-Performance on new works, different spellings, and damaged text can differ from the reported results.
+Open <http://127.0.0.1:8765>. The appearance is preserved. The eight-class option
+reports joint poem scores, while original packages can be loaded alongside it as
+separate dropdown strategies. Only compatible original models participate in their
+legacy ensemble. The local API is `POST /api/predict` with a `text` field.
 
 ## License and citation
 
-Project-owned material uses the [Chandassu citation license](https://huggingface.co/sprx7767/chandassu-recognition/blob/main/cnn/LICENSE).
-Cite Chandassu Recognition when you use the covered code or dataset material.
-Earlier Apache-2.0 permissions remain in effect.
-The ByT5 base encoder retains its Apache-2.0 license.
-
-Poems, editions, transcriptions, and source annotations retain their original rights and terms.
-Source rights have not been independently verified for every edition.
-Preserve poem authors and source links when you share data.
-See the [dataset rights statement](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/LICENSE.md).
-
-```bibtex
-@misc{pranav2026chandassu,
-  author = {Pranav Surampudi},
-  title = {Chandassu Recognition: Telugu Poetic Meter Classification},
-  year = {2026},
-  howpublished = {\url{https://github.com/Pranav-20186017/chandassu_recognition}},
-  note = {Code, dataset annotations, and trained-model results}
-}
-```
-
-[Citation metadata](https://huggingface.co/sprx7767/chandassu-recognition/blob/main/cnn/CITATION.cff)
+Project-owned code and weights follow the [Chandassu citation license](LICENSE).
+Earlier Apache-2.0 permissions remain in effect; the retained ByT5 base encoder is
+Apache-2.0. Poems, editions, transcriptions and annotations retain their source rights;
+these have not been independently cleared for every edition. Preserve authors and
+source links. See the project's dataset rights statement and [CITATION.cff](CITATION.cff).

cnn-eight-class/CITATION.cff

@@ -0,0 +1,18 @@
+cff-version: 1.2.0
+message: "Cite this project when you use its code or dataset. See LICENSE for the citation requirement."
+title: "Chandassu Recognition: Telugu Poetic Meter Classification"
+authors:
+  - family-names: "Surampudi"
+    given-names: "Pranav"
+type: software
+version: "1.0.0"
+date-released: "2026-10-11"
+url: "https://github.com/Pranav-20186017/chandassu_recognition"
+repository-code: "https://github.com/Pranav-20186017/chandassu_recognition"
+license-url: "https://github.com/Pranav-20186017/chandassu_recognition/blob/main/LICENSE"
+keywords:
+  - Telugu
+  - poetic meter
+  - CNN
+  - ByT5
+  - dataset

cnn-eight-class/LICENSE

@@ -0,0 +1,256 @@
+Chandassu Recognition Citation License, Version 1.0
+SPDX-License-Identifier: LicenseRef-Chandassu-Citation
+Copyright 2026 Pranav Surampudi
+
+1. Scope
+
+These terms apply to project-owned code, documentation, dataset organization,
+project-created annotations, and new project contributions in this revision.
+They apply only to rights that the project copyright holder can grant.
+Third-party poems, editions, transcriptions, annotations, software, and base
+model weights retain their respective rights and licenses. No permission for
+those materials is granted by this license. See data/LICENSE.md and NOTICE.
+
+2. Permission
+
+You may use, reproduce, modify, and distribute the covered project material,
+including for commercial purposes, subject to the citation requirements below
+and the Apache License, Version 2.0, reproduced below. For covered material,
+the citation requirements are additional conditions of this custom license.
+This license is not the unmodified Apache License, Version 2.0.
+
+3. Required citation
+
+Every use of covered project code or covered dataset material must retain this
+credit in the user's project documentation:
+
+Pranav Surampudi (2026). Chandassu Recognition: Telugu Poetic Meter Classification.
+https://github.com/Pranav-20186017/chandassu_recognition
+
+If the use produces a publication, report, application, model, or dataset,
+include that citation in its references, credits, or accompanying documentation.
+Private use must retain the citation in internal project documentation.
+The citation must identify the version or commit used when available.
+CITATION.cff and the README BibTeX entry provide the citation details.
+Do not represent this requirement as optional or remove it from redistributed
+covered material. A citation does not imply endorsement by the project author.
+
+4. Redistribution and source attribution
+
+Include this license and the applicable NOTICE with distributed covered
+material and derivative works. Preserve third-party copyright notices,
+licenses, poem authors, work titles, and source links. Project citation does
+not replace attribution or permission required by a third-party source.
+
+5. Earlier releases and third-party rights
+
+Material previously received under Apache-2.0 remains usable under that
+license. This revision does not revoke permissions already granted for earlier
+releases. Reused material that independently remains available under an earlier
+license retains those permissions. These additional terms govern the new
+project contributions and rights granted under this custom revision.
+
+The underlying Apache terms, including their patent provisions, warranty
+disclaimer, and limitation of liability, follow.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

cnn-eight-class/LICENSES/Apache-2.0.txt

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

cnn-eight-class/LICENSES/LicenseRef-Chandassu-Citation.txt

@@ -0,0 +1,256 @@
+Chandassu Recognition Citation License, Version 1.0
+SPDX-License-Identifier: LicenseRef-Chandassu-Citation
+Copyright 2026 Pranav Surampudi
+
+1. Scope
+
+These terms apply to project-owned code, documentation, dataset organization,
+project-created annotations, and new project contributions in this revision.
+They apply only to rights that the project copyright holder can grant.
+Third-party poems, editions, transcriptions, annotations, software, and base
+model weights retain their respective rights and licenses. No permission for
+those materials is granted by this license. See data/LICENSE.md and NOTICE.
+
+2. Permission
+
+You may use, reproduce, modify, and distribute the covered project material,
+including for commercial purposes, subject to the citation requirements below
+and the Apache License, Version 2.0, reproduced below. For covered material,
+the citation requirements are additional conditions of this custom license.
+This license is not the unmodified Apache License, Version 2.0.
+
+3. Required citation
+
+Every use of covered project code or covered dataset material must retain this
+credit in the user's project documentation:
+
+Pranav Surampudi (2026). Chandassu Recognition: Telugu Poetic Meter Classification.
+https://github.com/Pranav-20186017/chandassu_recognition
+
+If the use produces a publication, report, application, model, or dataset,
+include that citation in its references, credits, or accompanying documentation.
+Private use must retain the citation in internal project documentation.
+The citation must identify the version or commit used when available.
+CITATION.cff and the README BibTeX entry provide the citation details.
+Do not represent this requirement as optional or remove it from redistributed
+covered material. A citation does not imply endorsement by the project author.
+
+4. Redistribution and source attribution
+
+Include this license and the applicable NOTICE with distributed covered
+material and derivative works. Preserve third-party copyright notices,
+licenses, poem authors, work titles, and source links. Project citation does
+not replace attribution or permission required by a third-party source.
+
+5. Earlier releases and third-party rights
+
+Material previously received under Apache-2.0 remains usable under that
+license. This revision does not revoke permissions already granted for earlier
+releases. Reused material that independently remains available under an earlier
+license retains those permissions. These additional terms govern the new
+project contributions and rights granted under this custom revision.
+
+The underlying Apache terms, including their patent provisions, warranty
+disclaimer, and limitation of liability, follow.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
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cnn-eight-class/NOTICE

@@ -0,0 +1,15 @@
+Chandassu Recognition
+Copyright 2026 Pranav Surampudi
+
+Project-owned material in this revision uses LicenseRef-Chandassu-Citation.
+See LICENSE for the required citation and the complete terms.
+Earlier Apache-2.0 permissions remain in effect for earlier releases.
+
+Required credit:
+Pranav Surampudi (2026). Chandassu Recognition: Telugu Poetic Meter Classification.
+https://github.com/Pranav-20186017/chandassu_recognition
+
+ByT5 base encoder weights originate from google/byt5-small and retain their
+Apache-2.0 license. Its text is in LICENSES/Apache-2.0.txt.
+Prepared poems, editions, transcriptions, and source annotations retain their
+source rights. See data/LICENSE.md and data/v1/works.json.

cnn-eight-class/README.md

@@ -0,0 +1,29 @@
+---
+language: te
+license: other
+license_name: chandassu-citation
+license_link: LICENSE
+library_name: transformers
+pipeline_tag: text-classification
+tags: [telugu, poetry, metre, cnn, eight-class]
+---
+# Eight-class ordered poem CNN
+
+Fresh seed-17 CNN, selected at epoch 11. Test poem Macro F1 **94.84%**;
+accuracy **97.90%**. Ataveladi F1 **69.34%** is the principal weakness.
+
+Use `AutoTokenizer` and `AutoModelForSequenceClassification` with
+`subfolder="cnn-eight-class"` and `trust_remote_code=True`. Pass a complete
+newline-separated poem to the tokenizer, not individual lines. Tensors have shape
+`[batch, 4 ordered padas, tokens]`; output is one eight-class logit vector per poem.
+Scores are **uncalibrated**. Do not average these logits as line predictions.
+
+Four full padas, eight Seesamu body half-lines, or eight body half-lines plus four
+closing geeti lines are supported. The closing geeti never enters the CNN.
+Maximum length is 512 characters per full pada; input is never silently truncated.
+A batch is a list of complete poem strings. Standard line-level pipelines are
+unsupported; use the Auto interfaces and explicit whole-poem inputs.
+
+The native project loader and `inference.py` also support this exact package.
+See the [main model card](https://huggingface.co/sprx7767/chandassu-recognition)
+for usage, eight-label mapping, evaluation, source limitations and citation terms.

cnn-eight-class/config.json

@@ -0,0 +1,59 @@
+{
+  "architecture": "ordered_poem_cnn_v1",
+  "architectures": [
+    "ChandassuPoemCNNForSequenceClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "hf_poem.ChandassuPoemCNNConfig",
+    "AutoModel": "hf_poem.ChandassuPoemCNNForSequenceClassification",
+    "AutoModelForSequenceClassification": "hf_poem.ChandassuPoemCNNForSequenceClassification"
+  },
+  "calibrated": false,
+  "dropout": 0.2,
+  "id2label": {
+    "0": "ఉత్పలమాల",
+    "1": "చంపకమాల",
+    "2": "మత్తేభము",
+    "3": "శార్దూలం",
+    "4": "ఆటవెలది",
+    "5": "తేటగీతి",
+    "6": "కందము",
+    "7": "సీసము"
+  },
+  "kernel_sizes": [
+    3,
+    5,
+    7
+  ],
+  "label2id": {
+    "ఆటవెలది": 4,
+    "ఉత్పలమాల": 0,
+    "కందము": 6,
+    "చంపకమాల": 1,
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+    "మత్తేభము": 2,
+    "శార్దూలం": 3,
+    "సీసము": 7
+  },
+  "labels": [
+    "ఉత్పలమాల",
+    "చంపకమాల",
+    "మత్తేభము",
+    "శార్దూలం",
+    "ఆటవెలది",
+    "తేటగీతి",
+    "కందము",
+    "సీసము"
+  ],
+  "max_tokens": 512,
+  "model_type": "chandassu_poem_cnn",
+  "pad_token_id": 0,
+  "prediction_unit": "poem",
+  "representation": "Four ordered padas; Seesamu body only",
+  "run_id": "4e4a52c8970f00812f0ba5aee3818b9831f560353b94288ee553bdfa21386255",
+  "seed": 17,
+  "temperature": 1.0,
+  "training_data_manifest": "e73dd42275930a8ab3dfc6db64f575e6c0dc8fad83d295af66a0711381ff5a22",
+  "vocab_size": 104,
+  "width": 64
+}

cnn-eight-class/evaluation.json

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+  },
+  "run_id": "4e4a52c8970f00812f0ba5aee3818b9831f560353b94288ee553bdfa21386255",
+  "scope": "Historical v2 benchmark plus new-label held-out works; source annotations, not expert gold; no claim of blind historical test, calibration, rule verification or unseen-author generalization",
+  "selected_before_test": true
+}

cnn-eight-class/export_verification.json

@@ -0,0 +1,11 @@
+{
+  "complete_test_confusion_matrix_preserved": true,
+  "project_required_by_hf_loader": false,
+  "root_and_subfolder_exact_logits": true,
+  "test_accuracy": 0.9789852328663385,
+  "test_macro_f1": 0.9484251702907207,
+  "test_poems": 5282,
+  "validation_used_for_selection": true,
+  "weight_sha256": "cb94cb037b13ad8ff2e4e773fa6a80a5291537c10c4a978634717fbd37f12eeb",
+  "weights_unchanged": true
+}

cnn-eight-class/hf_poem.py

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+"""Standalone Transformers loading for the ordered eight-class poem CNN.
+
+Copyright 2026 Pranav Surampudi. SPDX-License-Identifier: LicenseRef-Chandassu-Citation
+Depends only on PyTorch, Transformers and the standard library.
+"""
+
+import json
+import shutil
+import unicodedata
+from pathlib import Path
+
+import torch
+from torch import nn
+from transformers import BatchEncoding, PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+LABELS = ["ఉత్పలమాల", "చంపకమాల", "మత్తేభము", "శార్దూలం", "ఆటవెలది", "తేటగీతి", "కందము", "సీసము"]
+
+
+class ChandassuPoemCNNConfig(PretrainedConfig):
+    model_type = "chandassu_poem_cnn"
+
+    def __init__(self, vocab_size=2, width=64, kernel_sizes=None, dropout=0.2, labels=None, max_tokens=512, **kwargs):
+        labels = LABELS if labels is None else labels
+        if labels != LABELS:
+            raise ValueError("Requires the eight trained labels in their saved order")
+        kwargs.setdefault("id2label", dict(enumerate(labels)))
+        kwargs.setdefault("label2id", {label: i for i, label in enumerate(labels)})
+        kwargs.setdefault("pad_token_id", 0)
+        super().__init__(**kwargs)
+        self.labels = labels
+        self.vocab_size = vocab_size
+        self.width = width
+        self.kernel_sizes = kernel_sizes or [3, 5, 7]
+        self.dropout = dropout
+        self.max_tokens = max_tokens
+        self.architecture = "ordered_poem_cnn_v1"
+        self.calibrated = False
+        self.prediction_unit = "poem"
+        self.temperature = 1.0
+
+
+def normalize(text):
+    return " ".join(unicodedata.normalize("NFC", text).replace("\ufeff", "").split())
+
+
+class ChandassuPoemTokenizer(PreTrainedTokenizer):
+    """Whole-poem tokenizer: tensors have shape [batch, 4 ordered padas, tokens].
+
+    Pass one newline-separated poem string or a batch of complete poem strings.
+    Four full padas, eight Seesamu body half-lines, and 8+4 Seesamu layouts are
+    supported. In the latter case only the first eight body half-lines are used.
+    """
+
+    vocab_files_names = {"vocabulary_file": "vocabulary.json"}
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(self, vocabulary_file, **kwargs):
+        self.vocabulary_file = str(vocabulary_file)
+        saved = json.loads(Path(vocabulary_file).read_text())
+        if (
+            not saved
+            or any(not isinstance(c, str) or len(c) != 1 for c in saved)
+            or any(type(i) is not int for i in saved.values())
+            or sorted(saved.values()) != list(range(2, len(saved) + 2))
+        ):
+            raise ValueError("Invalid character vocabulary")
+        self.tokens = {"[PAD]": 0, "[UNK]": 1, **saved}
+        self.reverse_tokens = {i: c for c, i in self.tokens.items()}
+        self.limit = kwargs.get("model_max_length", 512)
+        kwargs.setdefault("model_max_length", self.limit)
+        kwargs.setdefault("pad_token", "[PAD]")
+        kwargs.setdefault("unk_token", "[UNK]")
+        kwargs.setdefault("padding_side", "right")
+        super().__init__(**kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self.tokens)
+
+    def get_vocab(self):
+        return dict(self.tokens, **self.added_tokens_encoder)
+
+    def tokenize(self, text, **kwargs):
+        text = normalize(text)
+        if not text or len(text) > self.limit:
+            raise ValueError(f"Pada length outside 1..{self.limit}; no truncation")
+        return list(text)
+
+    def _tokenize(self, text):
+        return self.tokenize(text)
+
+    def _convert_token_to_id(self, token):
+        return self.tokens.get(token, 1)
+
+    def _convert_id_to_token(self, index):
+        return self.reverse_tokens.get(index, "[UNK]")
+
+    def convert_tokens_to_string(self, tokens):
+        return "".join(tokens)
+
+    def __call__(
+        self, text, text_pair=None, padding=True, truncation=False, max_length=None, return_tensors="pt", **kwargs
+    ):
+        if text_pair is not None:
+            raise ValueError("Text pairs are unsupported; supply a complete poem")
+        if truncation not in (None, False, "do_not_truncate"):
+            raise ValueError("Truncation is unsupported; supply complete padas")
+        if return_tensors not in (None, "pt"):
+            raise ValueError("Use return_tensors='pt' or None")
+        if kwargs.get("is_split_into_words") or kwargs.get("stride") or kwargs.get("return_overflowing_tokens"):
+            raise ValueError("Split words, stride and overflowing chunks cannot represent a complete poem")
+        if self.padding_side != "right" or kwargs.get("padding_side", "right") != "right":
+            raise ValueError("The trained CNN requires right padding")
+        poems = [text] if isinstance(text, str) else text
+        if not isinstance(poems, (list, tuple)) or not poems or any(not isinstance(p, str) for p in poems):
+            raise ValueError("Supply a poem string or a list of complete poem strings")
+        encoded = []
+        for poem in poems:
+            lines = [normalize(t) for t in poem.splitlines() if normalize(t)]
+            if len(lines) not in {4, 8, 12}:
+                raise ValueError(
+                    "Supply four full ordered padas, or eight Seesamu body half-lines "
+                    "optionally followed by four closing geeti lines"
+                )
+            if len(lines) != 4:
+                lines = [" ".join(lines[i : i + 2]) for i in range(0, 8, 2)]
+            encoded.append([[self.tokens.get(c, 1) for c in self.tokenize(line)] for line in lines])
+        longest = max(len(line) for poem in encoded for line in poem)
+        if max_length is not None and (type(max_length) is not int or max_length < longest):
+            raise ValueError("max_length cannot truncate a complete pada")
+        if padding in {False, None, "do_not_pad"}:
+            if return_tensors is not None and any(len(line) != longest for poem in encoded for line in poem):
+                raise ValueError("Use padding=True for mixed-length padas in a tensor batch")
+            length = None
+        elif padding in {True, "longest", "max_length"}:
+            length = (max_length or self.limit) if padding == "max_length" else longest
+        else:
+            raise ValueError("Unsupported padding mode")
+        if length is not None:
+            encoded = [[line + [0] * (length - len(line)) for line in poem] for poem in encoded]
+        masks = [[[int(t != 0) for t in line] for line in poem] for poem in encoded]
+        output = {"input_ids": encoded, "attention_mask": masks}
+        if return_tensors == "pt":
+            output = {k: torch.tensor(v, dtype=torch.long) for k, v in output.items()}
+        return BatchEncoding(output)
+
+    def encode(self, text, **kwargs):
+        return self(text, **dict(kwargs, return_tensors=None))["input_ids"][0]
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        path = Path(save_directory) / ((filename_prefix + "-" if filename_prefix else "") + "vocabulary.json")
+        if path.resolve() != Path(self.vocabulary_file).resolve():
+            shutil.copyfile(self.vocabulary_file, path)
+        return (str(path),)
+
+
+class ChandassuPoemCNNForSequenceClassification(PreTrainedModel):
+    """One uncalibrated eight-class logit vector per complete poem."""
+
+    config_class = ChandassuPoemCNNConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.embedding = nn.Embedding(config.vocab_size, config.width, padding_idx=0)
+        self.dropout = nn.Dropout(config.dropout)
+        self.convolutions = nn.ModuleList(
+            [nn.Conv1d(config.width, config.width, k, padding=k // 2) for k in config.kernel_sizes]
+        )
+        features = 2 * config.width * len(config.kernel_sizes)
+        self.classifier = nn.Sequential(
+            nn.Linear(4 * features, 128), nn.ReLU(), nn.Dropout(config.dropout), nn.Linear(128, len(config.labels))
+        )
+        self.post_init()
+
+    def forward(self, input_ids, attention_mask=None, labels=None, return_dict=None, **kwargs):
+        if input_ids.ndim != 3 or input_ids.shape[1] != 4:
+            raise ValueError("Expected [batch, 4 ordered padas, tokens]; tokenize complete poems")
+        valid = input_ids.ne(0)
+        if not valid.any(2).all():
+            raise ValueError("Empty pada")
+        if attention_mask is not None and not torch.equal(attention_mask.bool(), valid):
+            raise ValueError("Attention mask must match non-padding token IDs")
+        batch = input_ids.shape[0]
+        flat = input_ids.reshape(-1, input_ids.shape[-1])
+        mask = flat.ne(0)
+        x = self.dropout(self.embedding(flat).masked_fill(~mask.unsqueeze(-1), 0)).transpose(1, 2)
+        features = []
+        for conv in self.convolutions:
+            h = torch.relu(conv(x))
+            maximum = h.masked_fill(~mask.unsqueeze(1), float("-inf")).amax(2)
+            mean = h.masked_fill(~mask.unsqueeze(1), 0).sum(2) / mask.sum(1, keepdim=True)
+            features.extend([maximum, mean])
+        logits = self.classifier(self.dropout(torch.cat(features, 1).reshape(batch, -1)))
+        loss = nn.functional.cross_entropy(logits, labels) if labels is not None else None
+        if return_dict is False or (return_dict is None and not self.config.use_return_dict):
+            return (loss, logits) if loss is not None else (logits,)
+        return SequenceClassifierOutput(loss=loss, logits=logits)

cnn-eight-class/manifest.json

@@ -0,0 +1,10 @@
+{
+  "files": {
+    "config.json": "d56b49a05f27342b5ffa9d64f7f027ab5577bf2e0c02b7f23a7206d5cb7dd226",
+    "hf_poem.py": "685586f749074598d619d29c1d1262c30c3d69a00ac39d1f7539e5423d627a23",
+    "metadata.json": "7ea85e4fae87b5a4dc795f448fc48c84b67bcbaf6995cb5def3a6f13d3af6473",
+    "model.safetensors": "cb94cb037b13ad8ff2e4e773fa6a80a5291537c10c4a978634717fbd37f12eeb",
+    "tokenizer_config.json": "7a8ae5c27b87d81efacd44f49195cb0840bb0eccb41d73f14c360aa9a0bfbfd8",
+    "vocabulary.json": "650d957aa33d5e39a0a4cdd8c024002519f029ab183a944156132b24363a7b51"
+  }
+}

cnn-eight-class/metadata.json

@@ -0,0 +1,2884 @@
+{
+  "result.json": {
+    "calibrated": false,
+    "checkpoint_sha256": "903ff6f991ef20df7600fc5b93ff8608079c0864b38915c0f6d312b6cdfc29b5",
+    "epochs_run": 19,
+    "parameter_count": 266056,
+    "run_id": "4e4a52c8970f00812f0ba5aee3818b9831f560353b94288ee553bdfa21386255",
+    "selected_epoch": 11,
+    "selection_metrics": {
+      "accuracy": 0.8378116678770273,
+      "class_order": [
+        "ఉత్పలమాల",
+        "చంపకమాల",
+        "మత్తేభము",
+        "శార్దూలం",
+        "ఆటవెలది",
+        "తేటగీతి",
+        "కందము",
+        "సీసము"
+      ],
+      "confusion_matrix": [
+        [
+          581,
+          1,
+          1,
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+          0,
+          0,
+          1,
+          1
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+  "run_id": "4e4a52c8970f00812f0ba5aee3818b9831f560353b94288ee553bdfa21386255",
+  "seed": 17,
+  "temperature": 1.0,
+  "training_data_manifest": "e73dd42275930a8ab3dfc6db64f575e6c0dc8fad83d295af66a0711381ff5a22",
+  "vocab_size": 104,
+  "width": 64
+}

hf_poem.py

@@ -0,0 +1,198 @@
+"""Standalone Transformers loading for the ordered eight-class poem CNN.
+
+Copyright 2026 Pranav Surampudi. SPDX-License-Identifier: LicenseRef-Chandassu-Citation
+Depends only on PyTorch, Transformers and the standard library.
+"""
+
+import json
+import shutil
+import unicodedata
+from pathlib import Path
+
+import torch
+from torch import nn
+from transformers import BatchEncoding, PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+LABELS = ["ఉత్పలమాల", "చంపకమాల", "మత్తేభము", "శార్దూలం", "ఆటవెలది", "తేటగీతి", "కందము", "సీసము"]
+
+
+class ChandassuPoemCNNConfig(PretrainedConfig):
+    model_type = "chandassu_poem_cnn"
+
+    def __init__(self, vocab_size=2, width=64, kernel_sizes=None, dropout=0.2, labels=None, max_tokens=512, **kwargs):
+        labels = LABELS if labels is None else labels
+        if labels != LABELS:
+            raise ValueError("Requires the eight trained labels in their saved order")
+        kwargs.setdefault("id2label", dict(enumerate(labels)))
+        kwargs.setdefault("label2id", {label: i for i, label in enumerate(labels)})
+        kwargs.setdefault("pad_token_id", 0)
+        super().__init__(**kwargs)
+        self.labels = labels
+        self.vocab_size = vocab_size
+        self.width = width
+        self.kernel_sizes = kernel_sizes or [3, 5, 7]
+        self.dropout = dropout
+        self.max_tokens = max_tokens
+        self.architecture = "ordered_poem_cnn_v1"
+        self.calibrated = False
+        self.prediction_unit = "poem"
+        self.temperature = 1.0
+
+
+def normalize(text):
+    return " ".join(unicodedata.normalize("NFC", text).replace("\ufeff", "").split())
+
+
+class ChandassuPoemTokenizer(PreTrainedTokenizer):
+    """Whole-poem tokenizer: tensors have shape [batch, 4 ordered padas, tokens].
+
+    Pass one newline-separated poem string or a batch of complete poem strings.
+    Four full padas, eight Seesamu body half-lines, and 8+4 Seesamu layouts are
+    supported. In the latter case only the first eight body half-lines are used.
+    """
+
+    vocab_files_names = {"vocabulary_file": "vocabulary.json"}
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(self, vocabulary_file, **kwargs):
+        self.vocabulary_file = str(vocabulary_file)
+        saved = json.loads(Path(vocabulary_file).read_text())
+        if (
+            not saved
+            or any(not isinstance(c, str) or len(c) != 1 for c in saved)
+            or any(type(i) is not int for i in saved.values())
+            or sorted(saved.values()) != list(range(2, len(saved) + 2))
+        ):
+            raise ValueError("Invalid character vocabulary")
+        self.tokens = {"[PAD]": 0, "[UNK]": 1, **saved}
+        self.reverse_tokens = {i: c for c, i in self.tokens.items()}
+        self.limit = kwargs.get("model_max_length", 512)
+        kwargs.setdefault("model_max_length", self.limit)
+        kwargs.setdefault("pad_token", "[PAD]")
+        kwargs.setdefault("unk_token", "[UNK]")
+        kwargs.setdefault("padding_side", "right")
+        super().__init__(**kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self.tokens)
+
+    def get_vocab(self):
+        return dict(self.tokens, **self.added_tokens_encoder)
+
+    def tokenize(self, text, **kwargs):
+        text = normalize(text)
+        if not text or len(text) > self.limit:
+            raise ValueError(f"Pada length outside 1..{self.limit}; no truncation")
+        return list(text)
+
+    def _tokenize(self, text):
+        return self.tokenize(text)
+
+    def _convert_token_to_id(self, token):
+        return self.tokens.get(token, 1)
+
+    def _convert_id_to_token(self, index):
+        return self.reverse_tokens.get(index, "[UNK]")
+
+    def convert_tokens_to_string(self, tokens):
+        return "".join(tokens)
+
+    def __call__(
+        self, text, text_pair=None, padding=True, truncation=False, max_length=None, return_tensors="pt", **kwargs
+    ):
+        if text_pair is not None:
+            raise ValueError("Text pairs are unsupported; supply a complete poem")
+        if truncation not in (None, False, "do_not_truncate"):
+            raise ValueError("Truncation is unsupported; supply complete padas")
+        if return_tensors not in (None, "pt"):
+            raise ValueError("Use return_tensors='pt' or None")
+        if kwargs.get("is_split_into_words") or kwargs.get("stride") or kwargs.get("return_overflowing_tokens"):
+            raise ValueError("Split words, stride and overflowing chunks cannot represent a complete poem")
+        if self.padding_side != "right" or kwargs.get("padding_side", "right") != "right":
+            raise ValueError("The trained CNN requires right padding")
+        poems = [text] if isinstance(text, str) else text
+        if not isinstance(poems, (list, tuple)) or not poems or any(not isinstance(p, str) for p in poems):
+            raise ValueError("Supply a poem string or a list of complete poem strings")
+        encoded = []
+        for poem in poems:
+            lines = [normalize(t) for t in poem.splitlines() if normalize(t)]
+            if len(lines) not in {4, 8, 12}:
+                raise ValueError(
+                    "Supply four full ordered padas, or eight Seesamu body half-lines "
+                    "optionally followed by four closing geeti lines"
+                )
+            if len(lines) != 4:
+                lines = [" ".join(lines[i : i + 2]) for i in range(0, 8, 2)]
+            encoded.append([[self.tokens.get(c, 1) for c in self.tokenize(line)] for line in lines])
+        longest = max(len(line) for poem in encoded for line in poem)
+        if max_length is not None and (type(max_length) is not int or max_length < longest):
+            raise ValueError("max_length cannot truncate a complete pada")
+        if padding in {False, None, "do_not_pad"}:
+            if return_tensors is not None and any(len(line) != longest for poem in encoded for line in poem):
+                raise ValueError("Use padding=True for mixed-length padas in a tensor batch")
+            length = None
+        elif padding in {True, "longest", "max_length"}:
+            length = (max_length or self.limit) if padding == "max_length" else longest
+        else:
+            raise ValueError("Unsupported padding mode")
+        if length is not None:
+            encoded = [[line + [0] * (length - len(line)) for line in poem] for poem in encoded]
+        masks = [[[int(t != 0) for t in line] for line in poem] for poem in encoded]
+        output = {"input_ids": encoded, "attention_mask": masks}
+        if return_tensors == "pt":
+            output = {k: torch.tensor(v, dtype=torch.long) for k, v in output.items()}
+        return BatchEncoding(output)
+
+    def encode(self, text, **kwargs):
+        return self(text, **dict(kwargs, return_tensors=None))["input_ids"][0]
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        path = Path(save_directory) / ((filename_prefix + "-" if filename_prefix else "") + "vocabulary.json")
+        if path.resolve() != Path(self.vocabulary_file).resolve():
+            shutil.copyfile(self.vocabulary_file, path)
+        return (str(path),)
+
+
+class ChandassuPoemCNNForSequenceClassification(PreTrainedModel):
+    """One uncalibrated eight-class logit vector per complete poem."""
+
+    config_class = ChandassuPoemCNNConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.embedding = nn.Embedding(config.vocab_size, config.width, padding_idx=0)
+        self.dropout = nn.Dropout(config.dropout)
+        self.convolutions = nn.ModuleList(
+            [nn.Conv1d(config.width, config.width, k, padding=k // 2) for k in config.kernel_sizes]
+        )
+        features = 2 * config.width * len(config.kernel_sizes)
+        self.classifier = nn.Sequential(
+            nn.Linear(4 * features, 128), nn.ReLU(), nn.Dropout(config.dropout), nn.Linear(128, len(config.labels))
+        )
+        self.post_init()
+
+    def forward(self, input_ids, attention_mask=None, labels=None, return_dict=None, **kwargs):
+        if input_ids.ndim != 3 or input_ids.shape[1] != 4:
+            raise ValueError("Expected [batch, 4 ordered padas, tokens]; tokenize complete poems")
+        valid = input_ids.ne(0)
+        if not valid.any(2).all():
+            raise ValueError("Empty pada")
+        if attention_mask is not None and not torch.equal(attention_mask.bool(), valid):
+            raise ValueError("Attention mask must match non-padding token IDs")
+        batch = input_ids.shape[0]
+        flat = input_ids.reshape(-1, input_ids.shape[-1])
+        mask = flat.ne(0)
+        x = self.dropout(self.embedding(flat).masked_fill(~mask.unsqueeze(-1), 0)).transpose(1, 2)
+        features = []
+        for conv in self.convolutions:
+            h = torch.relu(conv(x))
+            maximum = h.masked_fill(~mask.unsqueeze(1), float("-inf")).amax(2)
+            mean = h.masked_fill(~mask.unsqueeze(1), 0).sum(2) / mask.sum(1, keepdim=True)
+            features.extend([maximum, mean])
+        logits = self.classifier(self.dropout(torch.cat(features, 1).reshape(batch, -1)))
+        loss = nn.functional.cross_entropy(logits, labels) if labels is not None else None
+        if return_dict is False or (return_dict is None and not self.config.use_return_dict):
+            return (loss, logits) if loss is not None else (logits,)
+        return SequenceClassifierOutput(loss=loss, logits=logits)

legacy-four-class/README.md

@@ -0,0 +1,296 @@
+---
+language:
+  - te
+license: other
+license_name: chandassu-citation
+license_link: https://huggingface.co/sprx7767/chandassu-recognition/blob/main/cnn/LICENSE
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+  - telugu
+  - poetry
+  - poetic-meter
+  - cnn
+  - byt5
+  - safetensors
+---
+
+# Chandassu Recognition
+
+**Author: Pranav Surampudi**
+
+Chandassu Recognition identifies four Telugu poetic meters:
+**ఉత్పలమాల · చంపకమాల · మత్తేభము · శార్దూలం**.
+
+The current CNN was trained on **NVIDIA GB10 (GX10)** using the expanded v2 corpus.
+The ByT5 package remains the original **NVIDIA H100 NVL** model on v1 data.
+This repository provides a character CNN and a ByT5 encoder classifier.
+Use the CNN when you need a smaller model.
+
+## Model packages
+
+| Model | Directory | Parameters | Seed | Selected epoch |
+|---|---|---:|---:|---:|
+| Character CNN | [cnn](https://huggingface.co/sprx7767/chandassu-recognition/tree/main/cnn) | 68,484 | 73 | 58 |
+| ByT5 encoder | [byt5](https://huggingface.co/sprx7767/chandassu-recognition/tree/main/byt5) | 217,663,364 | 17 | 17 |
+
+Each directory contains SafeTensors weights, configuration, input encoding, calibration settings, and model metadata.
+Use `AutoTokenizer` and `AutoModelForSequenceClassification` to load either model in a Python environment.
+You can also use the project loader and web interface.
+
+[Project code and instructions](https://github.com/Pranav-20186017/chandassu_recognition)
+
+## Architectures
+
+The CNN encodes 93 characters, plus padding and unknown tokens, with 64-dimensional embeddings.
+Three convolution branches use kernel sizes 3, 5, and 7.
+Each branch applies ReLU and masked max pooling to produce 64 features.
+A linear layer maps the combined 192 features to four class logits.
+
+The ByT5 model uses the encoder from [google/byt5-small](https://huggingface.co/google/byt5-small).
+The base revision is `68377bdc18a2ffec8a0533fef03b1c513a4dd49d`.
+The encoder has 12 layers and a hidden width of 1,472.
+Masked mean pooling and a linear classification layer produce four class logits.
+
+Both models classify individual lines.
+Temperature calibration adjusts the line probabilities.
+The mean of four line probability vectors gives the poem probabilities.
+The class with the highest probability is the poem prediction.
+
+[Architecture diagrams](https://github.com/Pranav-20186017/chandassu_recognition#model-architectures)
+
+## Data and sources
+
+The poems come from [AndhraBharati](https://andhrabharati.com/) and [Telugu Wikisource](https://te.wikisource.org/).
+The current CNN uses **13,295 complete poems**, **53,180 lines**, and **70 retained works**
+from v2, which already includes v1. ByT5 continues to use v1: 11,518 poems, 46,072 lines,
+and 36 retained works. The CNN update does not retrain or replace ByT5.
+
+| Use | CNN v2 poems / lines | ByT5 v1 poems / lines |
+|---|---:|---:|
+| Weight training | 7,575 / 30,300 | 6,517 / 26,068 |
+| Temperature calibration | 908 / 3,632 | 908 / 3,632 |
+| Validation | 1,859 / 7,436 | 1,859 / 7,436 |
+| Holdout test | 2,953 / 11,812 | 2,234 / 8,936 |
+| Total | 13,295 / 53,180 | 11,518 / 46,072 |
+
+All original v1 split assignments are preserved. Of 1,777 new poems from 34 works,
+1,058 poems from 27 works join fitting and 719 poems from seven works remain test.
+The new-work test allocation was frozen before training. One CNN test file combines
+the original 2,234 test poems and the 719 new-work test poems. Validation is unchanged.
+The 908 calibration poems are inside canonical train but never update model weights.
+
+Validation poem Macro F1 selects each checkpoint. Selected-checkpoint validation F1,
+then unweighted validation line cross-entropy, selects the released single CNN across
+seeds. Temperature is fitted separately for each model after checkpoint selection.
+Test data are excluded from fitting, vocabulary construction, selection, and calibration.
+All four ordered lines, work/edition families, and duplicate-linked works stay together.
+Authors may overlap. Source annotations and rule provenance are retained; these are
+not independently verified expert gold labels. Both CNN test cohorts were evaluated
+on earlier models: this is a fixed benchmark, not a new blind test or unseen-author study.
+
+The [v2 work catalogue](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/v2/WORKS.md)
+records the new works and their fitting/test assignments. The complete machine-readable
+catalogue includes 80 collected works, of which 70 retain supervised four-class poems.
+The [v1 catalogue](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/v1/WORKS.md)
+retains the original collection, including non-target and excluded material.
+
+## Training hyperparameters
+
+| Hyperparameter | CNN | ByT5 |
+|---|---:|---:|
+| Initial learning rate | 0.0001 | 0.00003 |
+| Microbatch size, poems | 16 | 8 |
+| Effective batch size, poems | 16 | 16 |
+| Evaluation batch size, poems | 16 | 8 |
+| Maximum epochs | 60 | 32 |
+| Minimum epochs | 24 | 8 |
+| Early stopping: epochs without improvement | 12 | 8 |
+| Input limit per line | 256 characters | 1,024 byte tokens, including the end token |
+| Gradient checkpointing | Off | Off |
+
+Both models use AdamW, weight decay 0.01, dropout 0.2, and gradient clipping 1.0.
+ReduceLROnPlateau uses validation line cross-entropy loss without class weights.
+The scheduler has patience 4, multiplier 0.5, and minimum learning rate 0.000001.
+The H100 study selected initial CNN LR 0.0001; the expanded GX10 run reuses it for
+seeds 17, 42, and 73 without another rate search or nested-fold study. All three CNNs
+share the same frozen partitions and architecture. They start from fresh weights.
+This repository contains the validation-selected CNN seed 73 and unchanged ByT5 seed 17.
+
+The exported calibration temperatures are **1.401754510250526** for CNN and **1.1801772437687996** for ByT5.
+CNN runtime: Python 3.12.3, PyTorch 2.10.0a0+b558c986e8.nv25.11, CUDA 13.0, NVIDIA GB10, BF16.
+Its training checkout was clean at `f7939ccac30d14bab929ed7d9c91aa202c5a9c80`.
+
+## Current CNN results
+
+| CNN seed | Selected epoch | Validation poem Macro F1 | Validation line CE before temperature | Temperature |
+|---|---:|---:|---:|---:|
+| 17 | 54 | 0.998596 | 0.031031 | 1.339218 |
+| 42 | 60 | 0.998596 | 0.031057 | 1.396669 |
+| 73 (released) | 58 | 0.998596 | 0.028452 | 1.401755 |
+
+Seed 73 wins the validation F1 tie through lower validation CE. Test results never
+select the seed. Every seed completed 60 epochs, followed by separate temperature
+calibration on the 908 calibration poems. Validation scores are checkpoint-selected.
+
+| Released CNN seed 73 benchmark | Poems | Correct | Poem Macro F1 | Line Macro F1 |
+|---|---:|---:|---:|---:|
+| Original v1 test | 2,234 | 2,234 | 1.000000 | 0.993992 |
+| Frozen new-work test | 719 | 716 | 0.996042 | 0.991346 |
+| Combined test | 2,953 | 2,950 | 0.999084 | 0.993420 |
+
+All three replicas and their predetermined equal-probability ensemble have the same
+combined-test poem score and the same three errors. Matched CPU inference with the
+previously released v1 CNN seed 17 produces the same poem predictions on these 2,953
+poems. There is **no measured poem-level gain** from this update. Combined-test line
+Macro F1 changes from 0.992608 (old CNN) to 0.993420 (new CNN); hardware/runtime,
+vocabulary, seed, and data differ, so this does not isolate the effect of corpus growth.
+The local three-seed ensemble line Macro F1 is 0.993592; it offers no poem-level gain.
+Ensemble membership and equal weights were fixed before evaluation.
+
+The returned checkpoints and native SafeTensors were compared tensor-for-tensor;
+file manifests, code identity, all split fingerprints, calibration assignments and
+checkpoint selection were verified. Mac CPU inference reproduces the test confusion
+matrices and F1 scores. The exported Transformers package preserves the selected weights.
+See the CNN directory's selection, test and verification reports.
+
+## Original H100 study (v1), retained for comparison
+
+| Original v1 measure | Original CNN | Unchanged ByT5 |
+|---|---:|---:|
+| Cross-validation poem Macro F1, mean across three seeds | 0.997130 | 0.997137 |
+| Correct historical test poems, seed 17 | 2,234 / 2,234 | 2,233 / 2,234 |
+| Historical test poem Macro F1, seed 17 | 1.000000 | 0.999619 |
+
+These original study scores do not describe a repeated cross-validation experiment
+on v2. The historical test had been examined during earlier project work. Labels
+combine source annotations and rule-based checks; experts have not verified every label.
+[Original study evidence](https://github.com/Pranav-20186017/chandassu_recognition/tree/main/results/v1)
+
+## Run inference
+
+### Python
+
+Install the tested dependencies in your Python environment:
+
+```bash
+pip install "torch==2.8.0" "transformers==4.57.6" "safetensors==0.8.0"
+```
+
+Use the Transformers Auto interfaces to load the tokenizer and classifier.
+The tokenizer applies the same text normalization and input encoding used in training.
+The model returns four raw logits for each line.
+Apply the saved calibration temperature, then average line probabilities to classify a poem.
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+repo = "sprx7767/chandassu-recognition"
+options = {"subfolder": "cnn", "trust_remote_code": True}
+# Change "cnn" to "byt5" to load the ByT5 encoder classifier.
+tokenizer = AutoTokenizer.from_pretrained(repo, **options)
+model = AutoModelForSequenceClassification.from_pretrained(repo, **options).eval()
+
+poem = """శ్రీ కైవల్య పదంబుఁ జేరుటకునై చింతించెదన్ లోక ర
+క్షైకారంభకు, భక్త పాలన కళా సంరంభకున్, దానవో
+ద్రేకస్తంభకుఁ, గేళి లోల విలసద్దృగ్జాల సంభూత నా
+నా కంజాత భవాండ కుంభకు, మహానందాంగనాడింభకున్."""
+lines = [line.strip() for line in poem.splitlines() if line.strip()]
+if len(lines) not in (1, 4):
+    raise ValueError("Supply one line or a four-line poem")
+inputs = tokenizer(lines, padding=True, return_tensors="pt")
+with torch.inference_mode():
+    logits = model(**inputs).logits
+    line_scores = (logits.double() / model.config.calibration_temperature).softmax(dim=-1)
+    scores = line_scores.mean(dim=0)
+
+winner = scores.argmax().item()
+print(model.config.id2label[winner], f"{scores[winner].item():.2%}")
+for index, score in enumerate(scores):
+    print(model.config.id2label[index], f"{score.item():.2%}")
+```
+
+`AutoModel.from_pretrained(repo, **options)` also loads this classifier.
+`trust_remote_code=True` loads the Python classes supplied in the model repository.
+For a fixed model version, add `"revision": "<full commit ID>"` to `options`.
+The example was tested with Python 3.12 and the dependency versions listed above.
+Input limits are 256 characters per line for CNN and 1,024 byte tokens, including the end token, for ByT5.
+The tokenizer rejects overlong input and does not truncate lines.
+
+Class probabilities are calibrated scores, not statistical confidence intervals.
+A standard text-classification pipeline applies its own softmax to line logits.
+Use the calculation above for calibrated poem scores.
+Clone this project when you need the web interface, ensembles, or training.
+
+### Web interface
+
+Install the project and its dependencies:
+
+```bash
+git clone https://github.com/Pranav-20186017/chandassu_recognition.git
+cd chandassu_recognition
+uv sync --locked --extra cpu
+source .venv/bin/activate
+```
+
+Download and serve the CNN:
+
+```bash
+hf download sprx7767/chandassu-recognition \
+  --include 'cnn/*' --local-dir models/hf
+
+python inference.py --model_dir=models/hf/cnn --strategy=single --port=8765
+```
+
+For ByT5, use these commands instead:
+
+```bash
+hf download sprx7767/chandassu-recognition \
+  --include 'byt5/*' --local-dir models/hf
+
+python inference.py --model_dir=models/hf/byt5 --strategy=single --port=8765
+```
+
+Open [http://127.0.0.1:8765](http://127.0.0.1:8765).
+Paste a single line or a four-line poem.
+The interface displays the meter, class probabilities, and line predictions.
+It supports light and dark themes.
+The API endpoint is `POST /api/predict` with a JSON request containing `text`.
+
+You can also train additional models with different seeds and hyperparameters.
+Use `--model_dirs` and `--strategy=ensemble` to load them together.
+Each ensemble requires distinct seeds from one model family.
+
+## Limitations
+
+Use these models to classify the four listed meters.
+They can assign high probabilities to poems from other meters.
+A high probability does not establish that an input belongs to a supported class.
+Class probabilities and score ranges are not statistical confidence intervals.
+The models do not produce syllable divisions, Guru/Laghu patterns, or gaṇa sequences.
+Performance on new works, different spellings, and damaged text can differ from the reported results.
+
+## License and citation
+
+Project-owned material uses the [Chandassu citation license](https://huggingface.co/sprx7767/chandassu-recognition/blob/main/cnn/LICENSE).
+Cite Chandassu Recognition when you use the covered code or dataset material.
+Earlier Apache-2.0 permissions remain in effect.
+The ByT5 base encoder retains its Apache-2.0 license.
+
+Poems, editions, transcriptions, and source annotations retain their original rights and terms.
+Source rights have not been independently verified for every edition.
+Preserve poem authors and source links when you share data.
+See the [dataset rights statement](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/LICENSE.md).
+
+```bibtex
+@misc{pranav2026chandassu,
+  author = {Pranav Surampudi},
+  title = {Chandassu Recognition: Telugu Poetic Meter Classification},
+  year = {2026},
+  howpublished = {\url{https://github.com/Pranav-20186017/chandassu_recognition}},
+  note = {Code, dataset annotations, and trained-model results}
+}
+```
+
+[Citation metadata](https://huggingface.co/sprx7767/chandassu-recognition/blob/main/cnn/CITATION.cff)

load_hf_model.py

@@ -0,0 +1,42 @@
+"""Load the public eight-class poem CNN and classify a UTF-8 poem text file.
+
+python scripts/load_hf_model.py --poem poem.txt --revision FULL_HF_COMMIT
+"""
+
+import argparse
+from pathlib import Path
+
+import torch
+from huggingface_hub import HfApi
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--repo_id", default="sprx7767/chandassu-recognition")
+    parser.add_argument("--revision", help="Immutable HF commit; if omitted, resolve main once and pin both loaders")
+    parser.add_argument("--subfolder", help="Optional explicit package: cnn-eight-class")
+    parser.add_argument("--poem", type=Path, required=True, help="Complete newline-separated poem in UTF-8")
+    parser.add_argument("--device", choices=["cpu", "cuda", "mps"], default="cpu")
+    args = parser.parse_args(argv)
+    revision = args.revision or HfApi().model_info(args.repo_id, token=False).sha
+    if len(revision) != 40 or any(c not in "0123456789abcdef" for c in revision):
+        parser.error("--revision must be a complete lowercase hexadecimal HF commit ID")
+    options = dict(revision=revision, trust_remote_code=True, token=False)
+    if args.subfolder:
+        options["subfolder"] = args.subfolder
+    tokenizer = AutoTokenizer.from_pretrained(args.repo_id, **options)
+    model = AutoModelForSequenceClassification.from_pretrained(args.repo_id, **options).to(args.device).eval()
+    if getattr(model.config, "prediction_unit", None) != "poem":
+        parser.error("This script requires the eight-class whole-poem model; original four-class models classify lines")
+    inputs = tokenizer(args.poem.read_text(encoding="utf-8"), padding=True, return_tensors="pt").to(args.device)
+    with torch.inference_mode():
+        scores = model(**inputs).logits.double().softmax(-1)[0]
+    print(f"Hugging Face revision: {revision}")
+    print(f"Prediction: {model.config.id2label[int(scores.argmax())]} (uncalibrated score)")
+    for i in scores.argsort(descending=True).tolist():
+        print(f"{model.config.id2label[i]}: {float(scores[i]):.4%}")
+
+
+if __name__ == "__main__":
+    main()

manifest.json

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+{
+  "files": {
+    "config.json": "d56b49a05f27342b5ffa9d64f7f027ab5577bf2e0c02b7f23a7206d5cb7dd226",
+    "hf_poem.py": "685586f749074598d619d29c1d1262c30c3d69a00ac39d1f7539e5423d627a23",
+    "metadata.json": "7ea85e4fae87b5a4dc795f448fc48c84b67bcbaf6995cb5def3a6f13d3af6473",
+    "model.safetensors": "cb94cb037b13ad8ff2e4e773fa6a80a5291537c10c4a978634717fbd37f12eeb",
+    "tokenizer_config.json": "7a8ae5c27b87d81efacd44f49195cb0840bb0eccb41d73f14c360aa9a0bfbfd8",
+    "vocabulary.json": "650d957aa33d5e39a0a4cdd8c024002519f029ab183a944156132b24363a7b51"
+  }
+}

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+    "scope": "Historical v2 benchmark plus new-label held-out works; source annotations, not expert gold; no claim of blind historical test, calibration, rule verification or unseen-author generalization",
+    "selected_before_test": true
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cb94cb037b13ad8ff2e4e773fa6a80a5291537c10c4a978634717fbd37f12eeb
+size 1065160

notebook.ipynb

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 {
-  "nbformat": 4,
-  "nbformat_minor": 5,
-  "metadata": {
-    "colab": {
-      "name": "Chandassu inference",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "license": "LicenseRef-Chandassu-Citation"
-  },
   "cells": [
     {
       "cell_type": "markdown",
-      "id": "introduction",
+      "id": "f12898a8d61a",
       "metadata": {},
       "source": [
-        "# Chandassu: Telugu poem meter recognition\n",
+        "# Chandassu: eight-class Telugu poem metre recognition\n",
         "\n",
-        "Use a fresh Colab runtime and run the two code cells below. This notebook uses\n",
-        "Colab's preinstalled packages; there is no installation cell.\n",
-        "CNN is selected by default and works on CPU. Choose `byt5` to use the original ByT5 model.\n",
+        "The default model is the new **eight-class ordered poem CNN**. It recognizes\n",
+        "ఉత్పలమాల, చంపకమాల, మత్తేభము, శార్దూలం, ఆటవెలది, తేటగీతి, కందము and సీసము.\n",
+        "It predicts once from all four ordered pādas; a Seesamu prediction uses its body\n",
+        "only. CPU is supported; CUDA is optional. No project checkout or login is required.\n",
         "\n",
-        "If you ran the earlier notebook that installed older PyTorch/Transformers packages,\n",
-        "first select **Runtime → Disconnect and delete runtime**, then reconnect.\n",
-        "Restarting Python alone does not undo those package installations.\n",
-        "[Colab runtime reset documentation](https://research.google.com/colaboratory/faq.html)\n",
+        "**Fixed held-out benchmark:** Macro F1 94.84%, accuracy 97.90%. Ataveladi F1 is\n",
+        "69.34%, the main weakness. These source-labelled tests include a historically\n",
+        "inspected benchmark; they are not independent expert gold or a wholly blind test.\n",
+        "Scores are **uncalibrated** and do not verify formal metre rules.\n",
         "\n",
-        "The model predicts **ఉత్పలమాల, చంపకమాల, మత్తేభము, శార్దూలం**.\n",
-        "Paste one line or a complete four-line poem into the prediction cell.\n"
+        "Run the setup, loader, and prediction cells in order. The loader resolves `main`\n",
+        "once and pins both tokenizer and model to that immutable commit. `trust_remote_code`\n",
+        "loads the published, inspectable `hf_poem.py` from this repository. The optional\n",
+        "original four-class example remains separate below.\n"
       ]
     },
     {
       "cell_type": "code",
-      "id": "load-model",
+      "id": "2f83c3532128",
+      "metadata": {
+        "tags": [
+          "dependencies"
+        ]
+      },
+      "source": [
+        "# Run once in a fresh notebook kernel. Keep Colab's preinstalled PyTorch.\n",
+        "%pip install -q \"transformers==4.57.6\" \"safetensors==0.8.0\" \"huggingface-hub==0.36.2\"\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "id": "31ee51cadf3a",
       "metadata": {},
       "source": [
-        "#@title Load a model\n",
-        "model_type = \"cnn\" #@param [\"cnn\", \"byt5\"]\n",
-        "\n",
         "import torch\n",
-        "import transformers\n",
-        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
+        "from huggingface_hub import HfApi\n",
+        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+        "\n",
+        "REPO_ID = \"sprx7767/chandassu-recognition\"\n",
+        "REVISION = None  # Optional: replace with a complete 40-character release commit.\n",
+        "SUBFOLDER = None  # Root defaults to eight classes; \"cnn-eight-class\" is equivalent.\n",
         "\n",
-        "repo = \"sprx7767/chandassu-recognition\"\n",
-        "options = {\n",
-        "    \"subfolder\": model_type,\n",
-        "    \"trust_remote_code\": True,\n",
-        "}\n",
+        "resolved_revision = REVISION or HfApi().model_info(REPO_ID, token=False).sha\n",
+        "options = {\"revision\": resolved_revision, \"trust_remote_code\": True, \"token\": False}\n",
+        "if SUBFOLDER:\n",
+        "    options[\"subfolder\"] = SUBFOLDER\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(REPO_ID, **options)\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(REPO_ID, **options).eval()\n",
         "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-        "tokenizer = AutoTokenizer.from_pretrained(repo, **options)\n",
-        "model = AutoModelForSequenceClassification.from_pretrained(\n",
-        "    repo, use_safetensors=True, **options\n",
-        ").to(device).eval()\n",
-        "print(f\"Loaded {model_type.upper()} seed {model.config.seed} on {device.upper()}\")\n",
-        "print(f\"PyTorch {torch.__version__}; Transformers {transformers.__version__}\")\n"
+        "model = model.to(device)\n",
+        "assert model.config.prediction_unit == \"poem\" and len(model.config.labels) == 8\n",
+        "print(\"HF commit:\", resolved_revision)\n",
+        "print(\"Device:\", device)\n",
+        "print(\"Labels:\", model.config.labels)\n"
       ],
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "id": "e2dffdfa4464",
+      "metadata": {},
+      "source": [
+        "## Enter a complete poem\n",
+        "\n",
+        "Use one newline-separated string containing **four full ordered pādas**.\n",
+        "For Seesamu, eight body half-lines are paired into four pādas; a 12-line input\n",
+        "(eight body half-lines plus four closing geeti lines) also works, and its geeti\n",
+        "is excluded. An eight-line input always means eight Seesamu body half-lines,\n",
+        "not four full body pādas plus four geeti lines.\n",
+        "\n",
+        "The limit is 512 Unicode characters per full pāda. Input is never silently\n",
+        "truncated. A batch is a list of complete poem strings, not a list of individual\n",
+        "pādas. Whole-poem tensors have shape `[batch, 4, tokens]`; logits have shape\n",
+        "`[batch, 8]`. There are no independent per-line scores to average.\n"
+      ]
+    },
     {
       "cell_type": "code",
-      "id": "predict-poem",
+      "id": "adb382bb6c60",
       "metadata": {},
       "source": [
-        "# Edit the text below, keeping each poem line on its own line, then run this cell.\n",
-        "poem = \"\"\"శ్రీ కైవల్య పదంబుఁ జేరుటకునై చింతించెదన్ లోక ర\n",
+        "POEM = \"\"\"శ్రీ కైవల్య పదంబుఁ జేరుటకునై చింతించెదన్ లోక ర\n",
         "క్షైకారంభకు, భక్త పాలన కళా సంరంభకున్, దానవో\n",
         "ద్రేకస్తంభకుఁ, గేళి లోల విలసద్దృగ్జాల సంభూత నా\n",
         "నా కంజాత భవాండ కుంభకు, మహానందాంగనాడింభకున్.\"\"\"\n",
         "\n",
-        "lines = [line.strip() for line in poem.splitlines() if line.strip()]\n",
-        "if len(lines) not in (1, 4):\n",
-        "    raise ValueError(\"Supply one line or a complete four-line poem\")\n",
-        "inputs = tokenizer(lines, padding=True, return_tensors=\"pt\").to(device)\n",
-        "with torch.inference_mode():\n",
-        "    logits = model(**inputs).logits\n",
-        "    line_scores = (logits.double() / model.config.calibration_temperature).softmax(dim=-1)\n",
-        "    scores = line_scores.mean(dim=0)\n",
-        "\n",
-        "winner = int(scores.argmax().item())\n",
-        "print(f\"Predicted meter: {model.config.id2label[winner]} ({scores[winner].item():.2%})\")\n",
-        "print(\"\\nClass probabilities:\")\n",
-        "for index, score in enumerate(scores):\n",
-        "    print(f\"  {model.config.id2label[index]}: {score.item():.2%}\")\n",
-        "print(\"\\nLine predictions:\")\n",
-        "for number, probabilities in enumerate(line_scores, start=1):\n",
-        "    index = int(probabilities.argmax().item())\n",
-        "    print(f\"  Line {number}: {model.config.id2label[index]} ({probabilities[index].item():.2%})\")\n"
+        "\n",
+        "def predict_poems(poems):\n",
+        "    inputs = tokenizer(poems, padding=True, return_tensors=\"pt\").to(device)\n",
+        "    with torch.inference_mode():\n",
+        "        probabilities = model(**inputs).logits.double().softmax(dim=-1).cpu()\n",
+        "    results = []\n",
+        "    for scores in probabilities:\n",
+        "        winner = int(scores.argmax())\n",
+        "        results.append({\n",
+        "            \"prediction\": model.config.id2label[winner],\n",
+        "            \"uncalibrated_scores\": {\n",
+        "                model.config.id2label[i]: float(scores[i])\n",
+        "                for i in scores.argsort(descending=True).tolist()\n",
+        "            },\n",
+        "        })\n",
+        "    return results\n",
+        "\n",
+        "\n",
+        "result = predict_poems(POEM)[0]\n",
+        "print(\"Prediction:\", result[\"prediction\"])\n",
+        "for label, score in result[\"uncalibrated_scores\"].items():\n",
+        "    print(f\"{label}: {score:.2%}\")\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "id": "5b00c2754e96",
+      "metadata": {},
+      "source": [
+        "# Optional: paste a Seesamu body as four full pādas or eight half-lines.\n",
+        "# A 12-line body-plus-geeti input is supported too; the final geeti is ignored.\n",
+        "SEESAMU = None\n",
+        "if SEESAMU:\n",
+        "    print(predict_poems(SEESAMU)[0])\n",
+        "\n",
+        "# For a batch, use predict_poems([POEM, ANOTHER_COMPLETE_POEM]).\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e32c59f04656",
+      "metadata": {},
+      "source": [
+        "## Benchmark and limits\n",
+        "\n",
+        "| New label | Fit | Validation | Held-out test | Test F1 |\n",
+        "|---|---:|---:|---:|---:|\n",
+        "| Ataveladi | 289 | 1,092 | 152 | 69.34% |\n",
+        "| Tetageeti | 943 | 209 | 640 | 92.27% |\n",
+        "| Kandamu | 2,571 | 721 | 1,071 | 99.63% |\n",
+        "| Seesamu body | 680 | 250 | 466 | 98.50% |\n",
+        "\n",
+        "The complete v3 corpus has 23,029 poems: 12,058 fit, 4,131 validation, 5,282 test,\n",
+        "and 1,558 reserved calibration poems (unused in this experiment). All original\n",
+        "v2 roles stay frozen. Whole poems and work/edition/exact-duplicate groups stay\n",
+        "together. Validation poem Macro F1 selected epoch 11; training stopped after 19.\n",
+        "The CPU model has 266,056 parameters. No ByT5 retraining was performed.\n",
+        "\n",
+        "More diverse Ataveladi fitting data is needed. Vemana dominates its validation\n",
+        "support; authors can overlap, and the exact-duplicate screen is not proof against\n",
+        "fuzzy duplicates. High scores do not validate prosody or reliable rejection of\n",
+        "unlisted metres. See [full reports](https://github.com/Pranav-20186017/chandassu_recognition/tree/main/results/v3).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bdddcacc85bf",
+      "metadata": {},
+      "source": [
+        "## Optional: original four-class line models\n",
+        "\n",
+        "The original `cnn/` and `byt5/` packages remain available. They classify individual\n",
+        "lines into the original four vruttas and average calibrated line probabilities\n",
+        "for poems. Keep this calculation separate from the eight-class joint CNN above.\n",
+        "ByT5 is a much larger download and is parked by default.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "id": "7da03b036678",
+      "metadata": {},
+      "source": [
+        "RUN_LEGACY = False\n",
+        "LEGACY_MODEL = \"cnn\"  # \"byt5\" is also retained; it downloads a much larger encoder.\n",
+        "\n",
+        "if RUN_LEGACY:\n",
+        "    legacy_options = {\"subfolder\": LEGACY_MODEL, \"revision\": resolved_revision,\n",
+        "                      \"trust_remote_code\": True, \"token\": False}\n",
+        "    legacy_tokenizer = AutoTokenizer.from_pretrained(REPO_ID, **legacy_options)\n",
+        "    legacy_model = AutoModelForSequenceClassification.from_pretrained(\n",
+        "        REPO_ID, **legacy_options).to(device).eval()\n",
+        "    lines = [line.strip() for line in POEM.splitlines() if line.strip()]\n",
+        "    if len(lines) not in (1, 4):\n",
+        "        raise ValueError(\"The original model accepts one line or four full pādas\")\n",
+        "    legacy_inputs = legacy_tokenizer(lines, padding=True, return_tensors=\"pt\").to(device)\n",
+        "    with torch.inference_mode():\n",
+        "        logits = legacy_model(**legacy_inputs).logits\n",
+        "        scores = (logits.double() / legacy_model.config.calibration_temperature).softmax(-1).mean(0)\n",
+        "    print(\"Original four-class prediction:\", legacy_model.config.id2label[int(scores.argmax())])\n",
+        "    for i, score in enumerate(scores):\n",
+        "        print(f\"{legacy_model.config.id2label[i]}: {float(score):.2%}\")\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "id": "d58d1813824b",
+      "metadata": {},
+      "source": [
+        "import platform\n",
+        "import transformers\n",
+        "\n",
+        "print({\"python\": platform.python_version(), \"torch\": torch.__version__,\n",
+        "       \"transformers\": transformers.__version__, \"huggingface_revision\": resolved_revision})\n"
       ],
       "execution_count": null,
       "outputs": []
     },
     {
       "cell_type": "markdown",
-      "id": "limits-citation",
+      "id": "0208ef2a8279",
       "metadata": {},
       "source": [
-        "`trust_remote_code=True` loads the current published custom tokenizer and classifier.\n",
-        "It does not install or replace Python packages.\n",
-        "\n",
-        "Predictions divide line logits by the model's saved calibration temperature, then\n",
-        "average line probabilities for a four-line poem. A generic pipeline's softmax does\n",
-        "not perform this calibration and poem aggregation.\n",
-        "\n",
-        "CNN accepts up to 256 characters per line. ByT5 accepts up to 1,024 UTF-8 byte tokens,\n",
-        "including the end token. The tokenizer rejects overlong input.\n",
-        "Scores are class probabilities, not confidence intervals. These models cover only\n",
-        "the four listed meters and do not generate prosodic explanations.\n",
-        "\n",
-        "See the [model card](https://huggingface.co/sprx7767/chandassu-recognition) for results,\n",
-        "provenance, and licensing. Cite Pranav Surampudi,\n",
-        "*Chandassu Recognition: Telugu Poetic Meter Classification* (2026).\n",
-        "[Project code](https://github.com/Pranav-20186017/chandassu_recognition) ·\n",
-        "[Citation metadata](https://huggingface.co/sprx7767/chandassu-recognition/blob/main/cnn/CITATION.cff)\n"
+        "## Sources and citation\n",
+        "\n",
+        "[Source and rule references](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/SOURCES.md) ·\n",
+        "[Work catalogue](https://github.com/Pranav-20186017/chandassu_recognition/blob/main/data/v3/WORKS.md) ·\n",
+        "[Model card](https://huggingface.co/sprx7767/chandassu-recognition) ·\n",
+        "[Project code](https://github.com/Pranav-20186017/chandassu_recognition)\n",
+        "\n",
+        "Author: Pranav Surampudi. Project-owned material follows the Chandassu citation\n",
+        "license. Poems, editions, transcriptions and annotations retain their source\n",
+        "rights. Preserve authors and source links. The examples use a pretrained\n",
+        "classifier; they do not implement a formal lakshana verifier or teacher distillation.\n"
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "colab": {
+      "name": "notebook.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
 }

release.json

@@ -0,0 +1,23 @@
+{
+  "calibrated": false,
+  "classes": 8,
+  "data_manifest_sha256": "e73dd42275930a8ab3dfc6db64f575e6c0dc8fad83d295af66a0711381ff5a22",
+  "github_commit": "662c990fff5d8e2c03fd277dd337c405baed6556",
+  "github_repository": "https://github.com/Pranav-20186017/chandassu_recognition",
+  "legacy_packages_preserved": [
+    "cnn/",
+    "byt5/",
+    "hf_model.py"
+  ],
+  "loading_script": "load_hf_model.py",
+  "model": "ordered_poem_cnn_v1",
+  "native_run_id": "4e4a52c8970f00812f0ba5aee3818b9831f560353b94288ee553bdfa21386255",
+  "notebook": "notebook.ipynb",
+  "python_loader": "hf_poem.py",
+  "seed": 17,
+  "selected_epoch": 11,
+  "test_accuracy": 0.9789852328663385,
+  "test_macro_f1": 0.9484251702907207,
+  "test_poems": 5282,
+  "weights_sha256": "cb94cb037b13ad8ff2e4e773fa6a80a5291537c10c4a978634717fbd37f12eeb"
+}

tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "auto_map": {
+    "AutoTokenizer": [
+      "hf_poem.ChandassuPoemTokenizer",
+      null
+    ]
+  },
+  "model_max_length": 512,
+  "padding_side": "right",
+  "tokenizer_class": "ChandassuPoemTokenizer"
+}

vocabulary.json

@@ -0,0 +1,104 @@
+{
+  " ": 2,
+  "!": 3,
+  "\"": 4,
+  "#": 5,
+  "'": 6,
+  "(": 7,
+  ")": 8,
+  ",": 9,
+  "-": 10,
+  ".": 11,
+  "0": 12,
+  "1": 13,
+  "2": 14,
+  "3": 15,
+  "4": 16,
+  "5": 17,
+  "6": 18,
+  "7": 19,
+  "8": 20,
+  "9": 21,
+  ":": 22,
+  ";": 23,
+  "?": 24,
+  "[": 25,
+  "\\": 26,
+  "|": 27,
+  "।": 28,
+  "॥": 29,
+  "ఁ": 30,
+  "ం": 31,
+  "ః": 32,
+  "అ": 33,
+  "ఆ": 34,
+  "ఇ": 35,
+  "ఈ": 36,
+  "ఉ": 37,
+  "ఊ": 38,
+  "ఋ": 39,
+  "ఎ": 40,
+  "ఏ": 41,
+  "ఐ": 42,
+  "ఒ": 43,
+  "ఓ": 44,
+  "ఔ": 45,
+  "క": 46,
+  "ఖ": 47,
+  "గ": 48,
+  "ఘ": 49,
+  "ఙ": 50,
+  "చ": 51,
+  "ఛ": 52,
+  "జ": 53,
+  "ఝ": 54,
+  "ఞ": 55,
+  "ట": 56,
+  "ఠ": 57,
+  "డ": 58,
+  "ఢ": 59,
+  "ణ": 60,
+  "త": 61,
+  "థ": 62,
+  "ద": 63,
+  "ధ": 64,
+  "న": 65,
+  "ప": 66,
+  "ఫ": 67,
+  "బ": 68,
+  "భ": 69,
+  "మ": 70,
+  "య": 71,
+  "ర": 72,
+  "ఱ": 73,
+  "ల": 74,
+  "ళ": 75,
+  "వ": 76,
+  "శ": 77,
+  "ష": 78,
+  "స": 79,
+  "హ": 80,
+  "ఽ": 81,
+  "ా": 82,
+  "ి": 83,
+  "ీ": 84,
+  "ు": 85,
+  "ూ": 86,
+  "ృ": 87,
+  "ె": 88,
+  "ే": 89,
+  "ై": 90,
+  "ొ": 91,
+  "ో": 92,
+  "ౌ": 93,
+  "్": 94,
+  "ౢ": 95,
+  "౦": 96,
+  "౽": 97,
+  "‌": 98,
+  "‍": 99,
+  "‘": 100,
+  "’": 101,
+  "“": 102,
+  "”": 103
+}