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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/environmental_2k |
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tags: |
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- ESG |
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- environmental |
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--- |
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# Model Card for EnvRoBERTa-environmental |
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## Model Description |
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Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the EnvRoBERTa-environmental language model. A language model that is trained to better classify environmental texts in the ESG domain. |
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*Note: We generally recommend choosing the [EnvironmentalBERT-environmental](https://huggingface.co/ESGBERT/EnvironmentalBERT-environmental) model since it is quicker, less resource-intensive and only marginally worse in performance.* |
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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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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
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tokenizer_name = "ESGBERT/EnvRoBERTa-environmental" |
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model_name = "ESGBERT/EnvRoBERTa-environmental" |
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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("Scope 1 emissions are reported here on a like-for-like basis against the 2013 baseline and exclude emissions from additional vehicles used during repairs.", padding=True, truncation=True)) |
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``` |
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## More details can be found in the paper |
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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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``` |
