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:
- d7b971288cfe36617e09c5845c65253554d7d26b0285152c39666aaa78f470ba
- Size of remote file:
- 3.57 kB
- SHA256:
- 954d55f31c5d5696dca1b5478370d21a1f35798e29a1cbfa7e06c4c1dd2893aa
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