Instructions to use CatBarks/bertES_ClassWeighted1_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CatBarks/bertES_ClassWeighted1_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CatBarks/bertES_ClassWeighted1_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CatBarks/bertES_ClassWeighted1_model") model = AutoModelForSequenceClassification.from_pretrained("CatBarks/bertES_ClassWeighted1_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af5059066b028f3d06fa9a9a3875a6c950ce945f3c92fb6980fbd027b0c42dc4
- Size of remote file:
- 438 MB
- SHA256:
- 0242bbde03d8f056681f21f17a0146b04274cec1128955ed6bc78effee4151c2
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