Instructions to use MENG21/studfacultyeval-DISTILIBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MENG21/studfacultyeval-DISTILIBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MENG21/studfacultyeval-DISTILIBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MENG21/studfacultyeval-DISTILIBERT") model = AutoModelForSequenceClassification.from_pretrained("MENG21/studfacultyeval-DISTILIBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MENG21/studfacultyeval-DISTILIBERT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/MENG21/studfacultyeval-DISTILIBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MENG21/studfacultyeval-DISTILIBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MENG21/studfacultyeval-DISTILIBERT/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 07dc3feb84cd1de65412660fd97df82fad95b6a5c18dc5b9d69eb46414f20b32
- Size of remote file:
- 438 MB
- SHA256:
- 7a0b712af08bf2ed8b12d88d97ff9fe414eff49b633b423ac9d454b4cf10020e
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