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 sample_input.pkl from palakagl/Roberta_Multiclass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/palakagl/Roberta_Multiclass_TextClassification/resolve/refs%2Fpr%2F1/sample_input.pkl
- Command line
-
hf download hf://palakagl/Roberta_Multiclass_TextClassification@refs/pr/1/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/palakagl/Roberta_Multiclass_TextClassification/resolve/refs%2Fpr%2F1/sample_input.pkl
2.03 kB
- Xet hash:
- e716218e057f8e83cc96afecdba952985733567c8e14bedd1ff5d0f305bdaf0f
- Size of remote file:
- 2.03 kB
- SHA256:
- 25e86fb7324f68aca0b111e31379580e7d74fac58d28561f44106b3a77f044a6
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