Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/roberta_Pfinal_4CLASES_2e-5_16_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/roberta_Pfinal_4CLASES_2e-5_16_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/roberta_Pfinal_4CLASES_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/roberta_Pfinal_4CLASES_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/roberta_Pfinal_4CLASES_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a8a69c32329bf67b4b773efff8b010bdfe11c74a947b583a4366a15509e781a
- Size of remote file:
- 3.64 kB
- SHA256:
- 0a9f5e6afb777851123c464c88a66b0706f4cd327947aa636efa1c1173a3fa6a
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