nguyen599/ViBERT-ESG-base
017
ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. ViBERT-ESG-base is a google-bert/bert-base-multilingual-cased model fine-tuned on ViEn-ESG-100 dataset, include 100,000 annotated sentences from Vietnam, English news and ESG reports.
Input: A financial text.
Output: Environmental, Social, Governance or None.
Language support: English, Vietnamese
How to use
You can use this model with Transformers pipeline for ESG classification.
# tested in transformers==4.51.0
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
esgbert = AutoModelForSequenceClassification.from_pretrained('nguyen599/ViBERT-ESG-base',num_labels=4)
tokenizer = AutoTokenizer.from_pretrained('nguyen599/ViBERT-ESG-base')
nlp = pipeline("text-classification", model=esgbert, tokenizer=tokenizer)
results = nlp('Over three chapters, it covers a range of topics from energy efficiency and renewable energy to the circular economy and sustainable transportation.')
print(results) # [{'label': 'Environment', 'score': 0.9206041026115417}]
Benchmark
F1 scores of models on each ESG category in the English ViEn-ESG-100 dataset.
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F1 scores of models on each ESG category in the Vietnamese ViEn-ESG-100 dataset.
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