Instructions to use mkbashar/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkbashar/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mkbashar/output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mkbashar/output") model = AutoModelForSequenceClassification.from_pretrained("mkbashar/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mkbashar/output: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/mkbashar/output/resolve/main/training_args.bin
- Command line
-
hf download hf://mkbashar/output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mkbashar/output/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- ec292873638d0bc3d39620472cf6a109d2e7c1d3cc94d63bbf1adc55b7e4e0cd
- Size of remote file:
- 4.73 kB
- SHA256:
- 3a4a0188517510deb68b1028469c7421e1525aa4efe2ae47fb5116d2f708f1d4
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