Instructions to use EMBEDDIA/bertic-tweetsentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBEDDIA/bertic-tweetsentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EMBEDDIA/bertic-tweetsentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/bertic-tweetsentiment") model = AutoModelForSequenceClassification.from_pretrained("EMBEDDIA/bertic-tweetsentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EMBEDDIA/bertic-tweetsentiment: direct link, hf CLI and curl.
- Browser
- Download file 2.61 kB
-
https://huggingface.co/EMBEDDIA/bertic-tweetsentiment/resolve/main/training_args.bin
- Command line
-
hf download hf://EMBEDDIA/bertic-tweetsentiment/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EMBEDDIA/bertic-tweetsentiment/resolve/main/training_args.bin
2.61 kB
- Xet hash:
- e452f238826852c711e148dd4808f52e5d26a1446f540409fd73de6cc8c41f77
- Size of remote file:
- 2.61 kB
- SHA256:
- ccc0e2df57a0c35ebbaf0be60b7c3129a766e88a0013311feb522b23023dc74b
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