README.md

---
language: en
license: apache-2.0
datasets:
- ESGBERT/WaterForestBiodiversityNature_2200
tags:
- ESG
- environmental
- biodiversity
---

# Model Card for EnvironmentalBERT-biodiversity

## Model Description

Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715), this is the EnvironmentalBERT-biodiversity language model. A language model that is trained to better classify biodiversity texts in the ESG/nature domain.

Using the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model as a starting point, the EnvironmentalBERT-biodiversity Language Model is additionally fine-trained on a 2.2k biodiversity dataset to detect biodiversity 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 "biodiversity" or not.

You can use the model with a pipeline for text classification:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
tokenizer_name = "ESGBERT/EnvironmentalBERT-biodiversity"
model_name = "ESGBERT/EnvironmentalBERT-biodiversity"
 
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("The majority of species are eliminated by modern agriculture techniques.", 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 Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold},
    year={2023},
    journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514},
}
```