Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use osanseviero/trainer-chapter3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osanseviero/trainer-chapter3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="osanseviero/trainer-chapter3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("osanseviero/trainer-chapter3") model = AutoModelForSequenceClassification.from_pretrained("osanseviero/trainer-chapter3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from osanseviero/trainer-chapter3: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/osanseviero/trainer-chapter3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://osanseviero/trainer-chapter3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/osanseviero/trainer-chapter3/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 285552eb3922bc257e02c4326dd45cf58fc1040f61cc848129baaf2c9be94372
- Size of remote file:
- 268 MB
- SHA256:
- 524225644437231c1331b33fa080b4b311270242bb37ba398954211feaa645dd
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