Text Classification
Transformers
PyTorch
English
roberta
ESG
environmental
text-embeddings-inference
Instructions to use ESGBERT/EnvRoBERTa-environmental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ESGBERT/EnvRoBERTa-environmental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ESGBERT/EnvRoBERTa-environmental")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ESGBERT/EnvRoBERTa-environmental") model = AutoModelForSequenceClassification.from_pretrained("ESGBERT/EnvRoBERTa-environmental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Using the [EnvRoBERTa-base](https://huggingface.co/ESGBERT/EnvRoBERTa-base) model as a starting point, the EnvRoBERTa-environmental Language Model is additionally fine-trained on a 2k environmental dataset to detect environmental text samples.
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## How to Get Started With the Model
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You can use the model with a pipeline for text classification:
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```python
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Using the [EnvRoBERTa-base](https://huggingface.co/ESGBERT/EnvRoBERTa-base) model as a starting point, the EnvRoBERTa-environmental Language Model is additionally fine-trained on a 2k environmental dataset to detect environmental text samples.
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## How to Get Started With the Model
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See these tutorials on Medium for a guide on [model usage](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-1-report-analysis-towards-esg-risks-and-opportunities-8daa2695f6c5?source=friends_link&sk=423e30ac2f50ee4695d258c2c4d54aa5), [large-scale analysis](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-2-large-scale-analyses-of-environmental-actions-0735cc8dc9c2?source=friends_link&sk=13a5aa1999fbb11e9eed4a0c26c40efa), and [fine-tuning](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-3-fine-tune-your-own-models-e3692fc0b3c0?source=friends_link&sk=49dc9f00768e43242fc1a76aa0969c70).
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You can use the model with a pipeline for text classification:
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```python
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