Text Classification
Transformers
PyTorch
TensorBoard
Habana
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use regisss/distilbert_xnli_hpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use regisss/distilbert_xnli_hpu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="regisss/distilbert_xnli_hpu")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("regisss/distilbert_xnli_hpu") model = AutoModelForSequenceClassification.from_pretrained("regisss/distilbert_xnli_hpu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from regisss/distilbert_xnli_hpu: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/regisss/distilbert_xnli_hpu/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://regisss/distilbert_xnli_hpu/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/regisss/distilbert_xnli_hpu/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 176cefa05d9815f505f6a13ae2556d32b20ebc88e228bff93680cb39132c984d
- Size of remote file:
- 541 MB
- SHA256:
- f1682594170d0aca3751028506f93d88d8679cd07a5ccf65da32634f44d49f1d
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