Instructions to use CatBarks/GPT2ES_ClassWeighted100_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CatBarks/GPT2ES_ClassWeighted100_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CatBarks/GPT2ES_ClassWeighted100_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CatBarks/GPT2ES_ClassWeighted100_model") model = AutoModelForSequenceClassification.from_pretrained("CatBarks/GPT2ES_ClassWeighted100_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7dbb4154de2898906e94809dc2a42c39625a4692ecb7cca943209b2fdc683782
- Size of remote file:
- 498 MB
- SHA256:
- 811d4deba025807076e8d6db9d1f5eed4dd1b7a88f0379ee1f3854b350190cfa
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