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 pytorch_model.bin from palakagl/distilbert_MultiClass_TextClassification: direct link, hf CLI and curl.
- Browser
- Download file 263 MB
-
https://huggingface.co/palakagl/distilbert_MultiClass_TextClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://palakagl/distilbert_MultiClass_TextClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/palakagl/distilbert_MultiClass_TextClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
263 MB
- Xet hash:
- c1d0866505ae1125d38c2a321d1852b0be4d85e8225118a971a1954e362928c4
- Size of remote file:
- 263 MB
- SHA256:
- f8efba5047cf68ae50ac21d69f2215f8d8c7078c1681906c7b572eb61d646b5e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.