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--- |
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language: en |
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license: apache-2.0 |
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datasets: |
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- ESGBERT/action_500 |
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tags: |
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- ESG |
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- environmental |
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- action |
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--- |
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# Model Card for EnvironmentalBERT-action |
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## Model Description |
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As an extension to [this paper](https://www.sciencedirect.com/science/article/pii/S1544612324000096), this is the EnvironmentalBERT-action language model. A language model that is trained to better classify action texts in the ESG domain. |
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Using the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model as a starting point, the EnvironmentalBERT-action Language Model is additionally fine-trained on a dataset with 500 sentences to detect action text samples. The underlying dataset is comparatively small, so if you would like to contribute to it, feel free to reach out. :) |
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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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It is highly recommended to first classify a sentence to be "environmental" or not with the [EnvironmentalBERT-environmental](https://huggingface.co/ESGBERT/EnvironmentalBERT-environmental) model before classifying whether it is "action" or not. This intersection allows us to build a targeted insight into whether the sentence displays an "environmental action". |
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You can use the model with a pipeline for text classification: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
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tokenizer_name = "ESGBERT/EnvironmentalBERT-action" |
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model_name = "ESGBERT/EnvironmentalBERT-action" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512) |
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pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) # set device=0 to use GPU |
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# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline |
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print(pipe("We are actively working to reduce our CO2 emissions by planting trees in 25 countries.", padding=True, truncation=True)) |
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``` |
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## More details to the base models can be found in this paper |
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While this dataset does not originate from the paper, it is a extension of it and the base models are described in it. |
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```bibtex |
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@article{schimanski_ESGBERT_2024, |
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title = {Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication}, |
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journal = {Finance Research Letters}, |
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volume = {61}, |
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pages = {104979}, |
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year = {2024}, |
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issn = {1544-6123}, |
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doi = {https://doi.org/10.1016/j.frl.2024.104979}, |
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url = {https://www.sciencedirect.com/science/article/pii/S1544612324000096}, |
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author = {Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold}, |
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} |
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``` |
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