Instructions to use CatBarks/GPT2ES_ClassWeighted2_1bce_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CatBarks/GPT2ES_ClassWeighted2_1bce_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CatBarks/GPT2ES_ClassWeighted2_1bce_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CatBarks/GPT2ES_ClassWeighted2_1bce_model") model = AutoModelForSequenceClassification.from_pretrained("CatBarks/GPT2ES_ClassWeighted2_1bce_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 778b7e0565ef1eb885b154e7442796bc2c4f121146a5dd22d704462c49a130f7
- Size of remote file:
- 498 MB
- SHA256:
- 594e3ab723050ba93231f8888ca51efe4ad68d1e3d4d62cd8b07d4a86badf27d
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