Instructions to use palakagl/Roberta_Multiclass_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/Roberta_Multiclass_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/Roberta_Multiclass_TextClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/Roberta_Multiclass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/Roberta_Multiclass_TextClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9d07c8538ba8e10e67417306761a345f82e69a6348d0f8af8d9dcf98db83035
- Size of remote file:
- 499 MB
- SHA256:
- c3ad58cf91c3bd90117a769f4ba95a34d871c6db66c0cd1348eac440481ad270
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