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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/Roberta_Multiclass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/Roberta_Multiclass_TextClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from palakagl/Roberta_Multiclass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/palakagl/Roberta_Multiclass_TextClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://palakagl/Roberta_Multiclass_TextClassification@refs/pr/1/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/palakagl/Roberta_Multiclass_TextClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- c6f1b9ede1c683272bdeef65f221764923378339550c657ef3cca35947ffeccd
- Size of remote file:
- 499 MB
- SHA256:
- 62c5c145820b5c92da88e222972a140a0fde6177afc626707f7f8c00330af38e
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