Instructions to use tom-beer/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tom-beer/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tom-beer/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tom-beer/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("tom-beer/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5ebff82f1251c82b313932774b4d7d24b1a499e75e70956607b817837b60e32
- Size of remote file:
- 268 MB
- SHA256:
- 37cee32666d4839963eb690260ca8b4cbd2146fb6f1e043bd7b893aa7fd99414
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