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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbroad/ESG-BERT") model = AutoModelForSequenceClassification.from_pretrained("nbroad/ESG-BERT") - Inference
- Notebooks
- Google Colab
- Kaggle
model documentation
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README.md
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# Model Card for ESG-BERT
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Domain Specific BERT Model for Text Mining in Sustainable Investing
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# Model Details
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## Model Description
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<details>
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<summary> Click to expand </summary>
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pip install torchserve torch-model-archiver
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pip install torchvision
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language:
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- en
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---
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# Model Card for ESG-BERT
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Domain Specific BERT Model for Text Mining in Sustainable Investing
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# Model Details
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## Model Description
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<details>
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<summary> Click to expand </summary>
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```
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pip install torchserve torch-model-archiver
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pip install torchvision
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