Update README.md

README.md

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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{Schimanski23ESGBERT,
+    title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}},
+    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},
+}
+```