Instructions to use karakaka/segmentation-pydec-segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karakaka/segmentation-pydec-segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="karakaka/segmentation-pydec-segmenter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("karakaka/segmentation-pydec-segmenter") model = AutoModelForTokenClassification.from_pretrained("karakaka/segmentation-pydec-segmenter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Trained on karakaka/segmentation-pydec-dataset-tokenized using karakaka/segmentation-pydec-mlm
Browse files
README.md
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# segmentation-pydec-segmenter
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This model is a fine-tuned version of [karakaka/segmentation-pydec-mlm](https://huggingface.co/karakaka/segmentation-pydec-mlm) on
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It achieves the following results on the evaluation set:
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- Loss: 0.1744
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- Precision: 0.6746
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# segmentation-pydec-segmenter
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This model is a fine-tuned version of [karakaka/segmentation-pydec-mlm](https://huggingface.co/karakaka/segmentation-pydec-mlm) on the karakaka/segmentation-pydec-dataset-tokenized dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1744
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- Precision: 0.6746
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