---
language: en
license: apache-2.0
datasets:
- ESGBERT/social_2k
tags:
- ESG
- social
---

# Model Card for SocRoBERTa-social

## Model Description

Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the SocRoBERTa-social language model. A language model that is trained to better classify social texts in the ESG domain.

*Note: We generally recommend choosing the [SocialBERT-social](https://huggingface.co/ESGBERT/SocialBERT-social) model since it is quicker, less resource-intensive and only marginally worse in performance.*

Using the [SocRoBERTa-base](https://huggingface.co/ESGBERT/SocRoBERTa-base) model as a starting point, the SocRoBERTa-social Language Model is additionally fine-trained on a 2k social dataset to detect social text samples.

## How to Get Started With the Model

See these tutorials on Medium for a guide on [model usage](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-1-report-analysis-towards-esg-risks-and-opportunities-8daa2695f6c5?source=friends_link&sk=423e30ac2f50ee4695d258c2c4d54aa5), [large-scale analysis](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-2-large-scale-analyses-of-environmental-actions-0735cc8dc9c2?source=friends_link&sk=13a5aa1999fbb11e9eed4a0c26c40efa), and [fine-tuning](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-3-fine-tune-your-own-models-e3692fc0b3c0?source=friends_link&sk=49dc9f00768e43242fc1a76aa0969c70).

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

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline

tokenizer_name = "ESGBERT/SocRoBERTa-social"
model_name = "ESGBERT/SocRoBERTa-social"
 
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 follow rigorous supplier checks to prevent slavery and ensure workers' rights.", 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 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},
}
```