Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use jethrowang/text_classification_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jethrowang/text_classification_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jethrowang/text_classification_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jethrowang/text_classification_model") model = AutoModelForSequenceClassification.from_pretrained("jethrowang/text_classification_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18b70c677e93aae5457ef22e243bbad5c69ec94fba67604a1739ae3888b90a09
- Size of remote file:
- 268 MB
- SHA256:
- 527ea9f44169d743a4fcf49f01ab94cb98f56a8421e104d4314dc19687ec8547
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