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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://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), 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 500 environmental dataset to detect action text samples. The underlying dataset is comparatively small, so if you 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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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{Schimanski23ESGBERT, |
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title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}}, |
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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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year={2023}, |
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journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514}, |
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} |
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``` |
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