Update README.md

README.md

@@ -1,3 +1,46 @@
 ---
+language: en
 license: apache-2.0
+datasets:
+- ESGBERT/governance_2k
+tags:
+- ESG
+- governance
 ---
+
+# Model Card for GovRoBERTa-governance
+
+## Model Description
+
+This is the GovRoBERTa-governance language model. A language model that is trained to better classify governance texts in the ESG domain.
+
+Using the [GovRoBERTa-base](https://huggingface.co/ESGBERT/GovRoBERTa-base) model as a starting point, the GovRoBERTa-governance Language Model is additionally fine-trained on a 2k governance dataset to detect governance text samples.
+
+## 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
+import datasets
+ 
+tokenizer_name = "ESGBERT/GovRoBERTa-governance"
+model_name = "ESGBERT/GovRoBERTa-governance"
+ 
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
+ 
+pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0)
+ 
+# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
+print(pipe("We also intend to improve both the monitoring (compliance) process of how our asset managers engage and engagement outcomes."))
+```
+
+## 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 Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold},
+    year={2023}
+}
+```