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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/WaterForestBiodiversityNature_2200 |
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tags: |
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- ESG |
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- environmental |
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- forest |
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--- |
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# Model Card for EnvironmentalBERT-water |
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## Model Description |
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Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715), this is the EnvironmentalBERT-forest language model. A language model that is trained to better classify forest texts in the ESG/nature domain. |
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Using the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model as a starting point, the EnvironmentalBERT-forest Language Model is additionally fine-trained on a 2.2k forest dataset to detect forest text samples. |
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## How to Get Started With the Model |
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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 "forest" or not. |
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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-forest" |
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model_name = "ESGBERT/EnvironmentalBERT-forest" |
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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("A large portion of trees in the Amazonas are dying each year.", 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{Schimanski23ExploringNature, |
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title={{Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures}}, |
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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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