Instructions to use ab3223323/esgBERTv1_Access_to_Communications with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ab3223323/esgBERTv1_Access_to_Communications with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ab3223323/esgBERTv1_Access_to_Communications")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ab3223323/esgBERTv1_Access_to_Communications") model = AutoModelForSequenceClassification.from_pretrained("ab3223323/esgBERTv1_Access_to_Communications", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ab3223323/esgBERTv1_Access_to_Communications: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/ab3223323/esgBERTv1_Access_to_Communications/resolve/main/optimizer.pt
- Command line
-
hf download hf://ab3223323/esgBERTv1_Access_to_Communications/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ab3223323/esgBERTv1_Access_to_Communications/resolve/main/optimizer.pt
876 MB
- Xet hash:
- 58c140de82aa0209a2214fb80949b07253787747857dfac38a4c9f8c197079f5
- Size of remote file:
- 876 MB
- SHA256:
- 15b4747b4498d867e3fa95c0e10afd06245539dd9aaae95de94d360db4ba383c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.