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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bee1abb9118af4f8060bf0b447c73a2b6d875bdca897ae527eb8f91ce5a26718
- Size of remote file:
- 439 MB
- SHA256:
- 3b8193ce97ddce307e10a5d618d35b02310f77b00bc96e4137b699cbaa1ae318
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.