Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use autoevaluate/multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/multi-class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/multi-class-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/multi-class-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/multi-class-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from autoevaluate/multi-class-classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/autoevaluate/multi-class-classification/resolve/refs%2Fpr%2F40/pytorch_model.bin
- Command line
-
hf download hf://autoevaluate/multi-class-classification@refs/pr/40/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/autoevaluate/multi-class-classification/resolve/refs%2Fpr%2F40/pytorch_model.bin
268 MB
- Xet hash:
- 8460128b07daeb693eb436b786479760d9fdd0bddcd7cf30299ebb2f08e57ada
- Size of remote file:
- 268 MB
- SHA256:
- 7cdebbd19c005f1af70a7d4940f83a7e1efdc208800619a114d8a1fa06cc28f1
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