Instructions to use nbroad/ESG-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/ESG-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbroad/ESG-BERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbroad/ESG-BERT") model = AutoModelForSequenceClassification.from_pretrained("nbroad/ESG-BERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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model documentation
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model documentation (#6)
- model documentation (3ef3dac7a9ea5b1b9baad635311d32be2041acf2) Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>0a1f547
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fix label spacing
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commit files to HF hub
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initial commit
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