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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mkbashar/output") model = AutoModelForSequenceClassification.from_pretrained("mkbashar/output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6d8b0b0d86eb6347630ddaacec8a2b19aa1b0f1ff3db085bf3cfb818bc48f23
- Size of remote file:
- 268 MB
- SHA256:
- bbf9199809d92185f9251d8a2184009677253818db3edf8ff9c39b37fbdf243a
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