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---
language: en
license: apache-2.0
datasets:
- ESGBERT/WaterForestBiodiversityNature_2200
tags:
- ESG
- environmental
- water
---
# Model Card for EnvironmentalBERT-water
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715), this is the EnvironmentalBERT-water language model. A language model that is trained to better classify water texts in the ESG/nature domain.
Using the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model as a starting point, the EnvironmentalBERT-water Language Model is additionally fine-trained on a 2.2k water dataset to detect water text samples.
## How to Get Started With the Model
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 "water" or not.
You can use the model with a pipeline for text classification:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
tokenizer_name = "ESGBERT/EnvironmentalBERT-water"
model_name = "ESGBERT/EnvironmentalBERT-water"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) # set device=0 to use GPU
# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
print(pipe("Water scarcity plays an increasing role in local communities in the South-West of the US.", padding=True, truncation=True))
```
## More details can be found in the paper
```bibtex
@article{Schimanski23ExploringNature,
title={{Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures}},
author={Tobias Schimanski and Chiara Colesanti Senni and Glen Gostlow and Jingwei Ni and Tingyu Yu and Markus Leippold},
year={2023},
journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715},
}
```
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