Instructions to use CatBarks/bertES_ClassWeighted10_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CatBarks/bertES_ClassWeighted10_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CatBarks/bertES_ClassWeighted10_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CatBarks/bertES_ClassWeighted10_model") model = AutoModelForSequenceClassification.from_pretrained("CatBarks/bertES_ClassWeighted10_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c9c9d8d97a1be0bb22b20adce9c3aed0528e0d3a198e5719c3b86384fbd597f
- Size of remote file:
- 438 MB
- SHA256:
- 3ab9f432cd0e3ef37a723d98b2b9c600911bbf9608386f07ec7b6bb6bc368a7a
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