Text Classification
Transformers
PyTorch
English
bert
financial-text-analysis
esg
environmental-social-corporate-governance
Instructions to use yiyanghkust/finbert-esg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yiyanghkust/finbert-esg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yiyanghkust/finbert-esg")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-esg") model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-esg") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#3
by joaogante - opened
Model converted by the transformers' pt_to_tf CLI.
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=6.676e-06; Maximum converted output difference=6.676e-06.