Instructions to use palakagl/distilbert_MultiClass_TextClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use palakagl/distilbert_MultiClass_TextClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="palakagl/distilbert_MultiClass_TextClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("palakagl/distilbert_MultiClass_TextClassification") model = AutoModelForSequenceClassification.from_pretrained("palakagl/distilbert_MultiClass_TextClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sample_input.pkl from palakagl/distilbert_MultiClass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/palakagl/distilbert_MultiClass_TextClassification/resolve/main/sample_input.pkl
- Command line
-
hf download hf://palakagl/distilbert_MultiClass_TextClassification/sample_input.pkl
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curl -L -o sample_input.pkl https://huggingface.co/palakagl/distilbert_MultiClass_TextClassification/resolve/main/sample_input.pkl
2.03 kB
- Xet hash:
- fbafcfc3784409b1e6b6a3bba5e6edbd2ba150a1bc6fd87d08f6ee510aa2293c
- Size of remote file:
- 2.03 kB
- SHA256:
- 357bf4d3a1e935fefa675d28f6884b50fedddf404a9bd9bd4720c412eb2f5e08
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