Instructions to use zeusfsx/instruction-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeusfsx/instruction-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="zeusfsx/instruction-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("zeusfsx/instruction-detection") model = AutoModelForTokenClassification.from_pretrained("zeusfsx/instruction-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Training
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## Evaluation
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## Training
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It's based on the transformer architecture and specifically uses the [xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) model from `ukr-models`, fine-tuned for the token classification task. The training data was carefully chosen to include a balanced distribution of titles containing instructions and those not containing instructions.
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The dataset contains newspaper titles (~3k titles), with tokens representing instructions manually labeled.
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## Evaluation
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