Instructions to use bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5") model = AutoModelForSequenceClassification.from_pretrained("bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5/resolve/main/training_args.bin
- Command line
-
hf download hf://bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bdpc/SciBERT_TwoWayLoss_25K_bs64_P10_N5/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 5c872ba2dd45b633f1981ec3d647c366ab5ea9528055a2bed58a7de5d8ab0ab2
- Size of remote file:
- 4.66 kB
- SHA256:
- 414c8bef0f21058aec54b38caa81235c491109c7a0706e5bc29155b0799a5c88
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