Instructions to use MENG21/studfacultyeval-BERT-BASE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MENG21/studfacultyeval-BERT-BASE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MENG21/studfacultyeval-BERT-BASE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MENG21/studfacultyeval-BERT-BASE") model = AutoModelForSequenceClassification.from_pretrained("MENG21/studfacultyeval-BERT-BASE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MENG21/studfacultyeval-BERT-BASE: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/MENG21/studfacultyeval-BERT-BASE/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MENG21/studfacultyeval-BERT-BASE/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MENG21/studfacultyeval-BERT-BASE/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 5883caf4e73a6445e79d1fb1314b2399b471c1d5498d6db96610b372267c77c3
- Size of remote file:
- 438 MB
- SHA256:
- 9fdd63881e4c5b21a2b363dad8f2451d2ca3bb251c30e2d831efdf3fe6654270
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