Instructions to use CatBarks/GPT2ES_ClassWeighted10_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CatBarks/GPT2ES_ClassWeighted10_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CatBarks/GPT2ES_ClassWeighted10_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CatBarks/GPT2ES_ClassWeighted10_model") model = AutoModelForSequenceClassification.from_pretrained("CatBarks/GPT2ES_ClassWeighted10_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1084ca5db0a2ee959382a138c9c9fb85a7b10cde2c935489b9db9877871335f
- Size of remote file:
- 498 MB
- SHA256:
- 1f3e8a88a2fd55672adfc27c2f8c61539e3bb0e7eb7c22d3af8fb7c1e6fb36dd
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