Instructions to use Tejas3/distillbert_base_uncased_80_equal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tejas3/distillbert_base_uncased_80_equal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tejas3/distillbert_base_uncased_80_equal")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tejas3/distillbert_base_uncased_80_equal") model = AutoModelForSequenceClassification.from_pretrained("Tejas3/distillbert_base_uncased_80_equal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tejas3/distillbert_base_uncased_80_equal: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Tejas3/distillbert_base_uncased_80_equal/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tejas3/distillbert_base_uncased_80_equal/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tejas3/distillbert_base_uncased_80_equal/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 8f80e9815d54d85c2f58315d9d30d1b5b7358de19004097f928463c803919db9
- Size of remote file:
- 268 MB
- SHA256:
- f7dee369dd3234cd2fd6a4f3b270170aceb2b2d5c786161673dbf51565c7e013
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