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
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py).
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.
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:00a31cdb3224ef0f8d6d64307b95392091d247396ca6b75f36c7a2c25ee1e48e
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size 439307552
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