Instructions to use ESGBERT/SocialBERT-social with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ESGBERT/SocialBERT-social with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ESGBERT/SocialBERT-social")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ESGBERT/SocialBERT-social") model = AutoModelForSequenceClassification.from_pretrained("ESGBERT/SocialBERT-social", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ESGBERT/SocialBERT-social: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/ESGBERT/SocialBERT-social/resolve/2b9e0fde8113bcc2bcfbd6b3ae214fd629003a42/pytorch_model.bin
- Command line
-
hf download hf://ESGBERT/SocialBERT-social@2b9e0fde8113bcc2bcfbd6b3ae214fd629003a42/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ESGBERT/SocialBERT-social/resolve/2b9e0fde8113bcc2bcfbd6b3ae214fd629003a42/pytorch_model.bin
329 MB
- Xet hash:
- 9191136d01a040c039bfe5589df2ef019c89990d97e75cca34f71f7cab4674c4
- Size of remote file:
- 329 MB
- SHA256:
- 3476ed6c1022d41d19e981fc7f7e2305f7223b371ef6581c38476902b5805ed9
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