Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use shed-e/ag_news-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/ag_news-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shed-e/ag_news-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shed-e/ag_news-Classification") model = AutoModelForSequenceClassification.from_pretrained("shed-e/ag_news-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shed-e/ag_news-Classification: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/shed-e/ag_news-Classification/resolve/main/training_args.bin
- Command line
-
hf download hf://shed-e/ag_news-Classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shed-e/ag_news-Classification/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 0c9a86962d4be03205e85f5294bd37b0170f540b4bc670d8732eab0e2ddf7b91
- Size of remote file:
- 3.31 kB
- SHA256:
- aa7c9e44a05e43d19ab465669b4cb853543f16dd9e637cf5563fde4d14aea4ed
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