Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use HanBi/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HanBi/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HanBi/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HanBi/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("HanBi/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 867d83a6fbba697750e0948308580330e99cf0a397145b19cb5d83e542dc83bf
- Size of remote file:
- 3.96 kB
- SHA256:
- 643638c5e7c9ebec3f974f883026e1cc431b014d6a2f9b89749462e5f673c820
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