Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use scbtm/phrasebank-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scbtm/phrasebank-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scbtm/phrasebank-sentiment-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scbtm/phrasebank-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("scbtm/phrasebank-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from scbtm/phrasebank-sentiment-analysis: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/scbtm/phrasebank-sentiment-analysis/resolve/main/training_args.bin
- Command line
-
hf download hf://scbtm/phrasebank-sentiment-analysis/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/scbtm/phrasebank-sentiment-analysis/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- e8a9db671f3a53a7ff8056c79d9dba68d6f1314119d411ab23ea6aa7c0d66477
- Size of remote file:
- 4.54 kB
- SHA256:
- f4833ba9cff0aa27ba3e9507795f1f26c7b8d87acf92be1f78bac724262690ab
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