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 training_args.bin from autoevaluate/multi-class-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/autoevaluate/multi-class-classification/resolve/refs%2Fpr%2F22/training_args.bin
- Command line
-
hf download hf://autoevaluate/multi-class-classification@refs/pr/22/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/autoevaluate/multi-class-classification/resolve/refs%2Fpr%2F22/training_args.bin
3.31 kB
- Xet hash:
- 1907c5bb0d2b437d2018431303c8f16cb831c852f80184ec6e9584b73e0ebcd9
- Size of remote file:
- 3.31 kB
- SHA256:
- 1933dc55337e5411c7cc0ce027d9d4fbc0c3845cd663ccac1b55461e7ea607e4
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