Instructions to use MENG21/studfacultyeval-BERT-LARGE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MENG21/studfacultyeval-BERT-LARGE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MENG21/studfacultyeval-BERT-LARGE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MENG21/studfacultyeval-BERT-LARGE") model = AutoModelForSequenceClassification.from_pretrained("MENG21/studfacultyeval-BERT-LARGE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MENG21/studfacultyeval-BERT-LARGE: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/MENG21/studfacultyeval-BERT-LARGE/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MENG21/studfacultyeval-BERT-LARGE/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MENG21/studfacultyeval-BERT-LARGE/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 89996cc39c141a846f1f7c3ff0ca3dc6bb271b6410ef263a1ad463168ab29eb1
- Size of remote file:
- 1.34 GB
- SHA256:
- 60d1f153a747b398f775bd2b2188ab2579f5939c66c0cc0b30d46458c2d97987
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