Update README.md

README.md

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 ---
+language: en
 license: apache-2.0
+datasets:
+- ESGBERT/action_500
+tags:
+- ESG
+- environmental
+- action
 ---
+
+# Model Card for EnvironmentalBERT-action
+
+## Model Description
+
+As an extension to [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the EnvironmentalBERT-action language model. A language model that is trained to better classify action texts in the ESG domain.
+
+Using the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model as a starting point, the EnvironmentalBERT-action Language Model is additionally fine-trained on a 500 environmental dataset to detect action text samples. The underlying dataset is comparatively small, so if you like to contribute to it, feel free to reach out.
+
+## How to Get Started With the Model
+You can use the model with a pipeline for text classification:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+ 
+tokenizer_name = "ESGBERT/EnvironmentalBERT-action"
+model_name = "ESGBERT/EnvironmentalBERT-action"
+ 
+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("We are actively working to reduce our CO2 emissions by planting trees in 25 countries.", padding=True, truncation=True))
+```
+
+## More details to the base models can be found in this paper
+
+While this dataset does not originate from the paper, it is a extension of it and the base models are described in it.
+
+```bibtex
+@article{Schimanski23ESGBERT,
+    title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}},
+    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},
+}
+```