Instructions to use ba-claim/distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ba-claim/distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ba-claim/distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ba-claim/distilbert") model = AutoModelForSequenceClassification.from_pretrained("ba-claim/distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ba-claim/distilbert: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/ba-claim/distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ba-claim/distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ba-claim/distilbert/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- add1efa22cc577a1726e3d10b55bff5fa13a29bdc4051d4f0251538c6e95725c
- Size of remote file:
- 268 MB
- SHA256:
- efde6cf49a204c8421d0b13d74a1e165a7fa7fa89f225f365fb9b4b87ac9c720
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