Text Classification
Transformers
PyTorch
English
roberta
ESG
environmental
text-embeddings-inference
Instructions to use ESGBERT/EnvRoBERTa-environmental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ESGBERT/EnvRoBERTa-environmental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ESGBERT/EnvRoBERTa-environmental")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ESGBERT/EnvRoBERTa-environmental") model = AutoModelForSequenceClassification.from_pretrained("ESGBERT/EnvRoBERTa-environmental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 889 Bytes
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language: en
license: apache-2.0
tags:
- ESG
- environmental
---
# Model Card for EnvRoBERTa-environmental
## Model Description
This is the EnvRoBERTa-environmental language model. A language model that is trained to better classify environmental texts in the ESG domain.
Using the [EnvRoBERTa-base](https://huggingface.co/ESGBERT/EnvRoBERTa-base) model as a starting point, the EnvRoBERTa-environmental Language Model is additionally fine-trained on a 2k environmental dataset to detect environmental text samples.
Get started:
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}
}
``` |