Text Classification
Transformers
PyTorch
English
roberta
ESG
environmental
text-embeddings-inference
Instructions to use ESGBERT/EnvRoBERTa-environmental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ESGBERT/EnvRoBERTa-environmental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ESGBERT/EnvRoBERTa-environmental")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ESGBERT/EnvRoBERTa-environmental") model = AutoModelForSequenceClassification.from_pretrained("ESGBERT/EnvRoBERTa-environmental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ESGBERT/EnvRoBERTa-environmental: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/ESGBERT/EnvRoBERTa-environmental/resolve/6cd314d0b2de9bddb3ae2845e4e402926022a7a5/pytorch_model.bin
- Command line
-
hf download hf://ESGBERT/EnvRoBERTa-environmental@6cd314d0b2de9bddb3ae2845e4e402926022a7a5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ESGBERT/EnvRoBERTa-environmental/resolve/6cd314d0b2de9bddb3ae2845e4e402926022a7a5/pytorch_model.bin
499 MB
- Xet hash:
- 26089992a6dd0e386526d43f7e529732ab70e6b95ab529ab95d4c81ddbc2bc0d
- Size of remote file:
- 499 MB
- SHA256:
- 184b271f7a90b6eec933284035c0d3c21c0cb21ec1c7a6e45aca9112368aa4bc
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