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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scbtm/phrasebank-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("scbtm/phrasebank-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbffc9b83f9cf1c22aee3a9eb6c653405aa5c435f19b4cebcf970f6dad24dd24
- Size of remote file:
- 438 MB
- SHA256:
- b31b3fb05c4cc6f9f43d1a4ea530115225693bf5848210181ed4ebcbf03ddc5f
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