Instructions to use EshAhm/sciBERT-RA-Context-Cross-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EshAhm/sciBERT-RA-Context-Cross-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EshAhm/sciBERT-RA-Context-Cross-Encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EshAhm/sciBERT-RA-Context-Cross-Encoder") model = AutoModelForSequenceClassification.from_pretrained("EshAhm/sciBERT-RA-Context-Cross-Encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EshAhm/sciBERT-RA-Context-Cross-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/EshAhm/sciBERT-RA-Context-Cross-Encoder/resolve/main/training_args.bin
- Command line
-
hf download hf://EshAhm/sciBERT-RA-Context-Cross-Encoder/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/EshAhm/sciBERT-RA-Context-Cross-Encoder/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 79a2f5fb87599debe9d09d96d6a4d685ccf2b62cff5c66e66a5ea339d9cde146
- Size of remote file:
- 5.2 kB
- SHA256:
- bd6be9dd2412ac1ac3bcb74ef017eb3299981278df2882162976e72bef89f53a
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