Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Saiteja/phrasebank-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saiteja/phrasebank-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Saiteja/phrasebank-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Saiteja/phrasebank-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("Saiteja/phrasebank-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae23a0b2a6d90741adf48f56237ae53e4a6a2f2a835eeadb7c118576a6cf44fd
- Size of remote file:
- 438 MB
- SHA256:
- 9c3b0b0298a771b83a9c82e9e39e503ebcce19028eacf7d8c28993d7d7de33e6
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