Instructions to use EshAhm/sciBERT-RA-Cross-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EshAhm/sciBERT-RA-Cross-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EshAhm/sciBERT-RA-Cross-Encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EshAhm/sciBERT-RA-Cross-Encoder") model = AutoModelForSequenceClassification.from_pretrained("EshAhm/sciBERT-RA-Cross-Encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EshAhm/sciBERT-RA-Cross-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/EshAhm/sciBERT-RA-Cross-Encoder/resolve/main/training_args.bin
- Command line
-
hf download hf://EshAhm/sciBERT-RA-Cross-Encoder/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EshAhm/sciBERT-RA-Cross-Encoder/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- f50212250083a66e3af908f70b9dc35d6cf90cf44fe950a02c83649b4936f72f
- Size of remote file:
- 5.2 kB
- SHA256:
- 45aede8009c46ff3b7f9d69f5bacfb58b77cac7f57084208e67996c940eadd23
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