Instructions to use Bennet1996/esgModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bennet1996/esgModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bennet1996/esgModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Bennet1996/esgModel") model = AutoModelForSequenceClassification.from_pretrained("Bennet1996/esgModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c51d468cad95112e8fa81c7d2483ce1fc509578e1bed64692a97ac20e98b88ba
- Size of remote file:
- 268 MB
- SHA256:
- 85d2941ad7e3cc2c4471dd9efbdbec9374e29b51eeb80dc064b68792425e3d06
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