Text Classification
Transformers
Safetensors
English
roberta
ESG
environmental
action
text-embeddings-inference
Instructions to use ESGBERT/EnvironmentalBERT-action with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ESGBERT/EnvironmentalBERT-action with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ESGBERT/EnvironmentalBERT-action")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ESGBERT/EnvironmentalBERT-action") model = AutoModelForSequenceClassification.from_pretrained("ESGBERT/EnvironmentalBERT-action", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 251e17bb0dfc577caeaeacb6349e0da7fca2917157de1b40fd96b1b8a5f29d28
- Size of remote file:
- 328 MB
- SHA256:
- 6a07ac758aaa81ee2f0ce8c7930e3c947150b2585f17e653d3cf2dc73360e06c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.