Instructions to use naveenVakada/tiny_model_imbalanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naveenVakada/tiny_model_imbalanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="naveenVakada/tiny_model_imbalanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("naveenVakada/tiny_model_imbalanced") model = AutoModelForSequenceClassification.from_pretrained("naveenVakada/tiny_model_imbalanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from naveenVakada/tiny_model_imbalanced: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/naveenVakada/tiny_model_imbalanced/resolve/main/training_args.bin
- Command line
-
hf download hf://naveenVakada/tiny_model_imbalanced/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/naveenVakada/tiny_model_imbalanced/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 7856b502e29643cac8d9c6bc7dc8f0b0c246d87cf290324c4cf3494cd889629b
- Size of remote file:
- 5.11 kB
- SHA256:
- 288de2e6227839c97bf283f9b743f754417847a903d97f35547af27b8d4840f1
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