Instructions to use regmisaugat59/BertFineTuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use regmisaugat59/BertFineTuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="regmisaugat59/BertFineTuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("regmisaugat59/BertFineTuning") model = AutoModelForSequenceClassification.from_pretrained("regmisaugat59/BertFineTuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from regmisaugat59/BertFineTuning: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/regmisaugat59/BertFineTuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://regmisaugat59/BertFineTuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/regmisaugat59/BertFineTuning/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- bce51b054b8fa1221490f4d64ac3abed340509b60ab1cfa8dd9c83ae943166a3
- Size of remote file:
- 438 MB
- SHA256:
- 8f385bf6eb356f395b223edaf1f006a0999c3c299c4b76d5328136295795ea19
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