Instructions to use syssec-utd/py312-pylingual-v1-segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py312-pylingual-v1-segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="syssec-utd/py312-pylingual-v1-segmenter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py312-pylingual-v1-segmenter") model = AutoModelForTokenClassification.from_pretrained("syssec-utd/py312-pylingual-v1-segmenter") - Notebooks
- Google Colab
- Kaggle
Trained on syssec-utd/segmentation-py312-pylingual-v1-tokenized using syssec-utd/py312-pylingual-v1-mlm
Browse files
README.md
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# py312-pylingual-v1-segmenter
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This model is a fine-tuned version of [syssec-utd/py312-pylingual-v1-mlm](https://huggingface.co/syssec-utd/py312-pylingual-v1-mlm) on
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It achieves the following results on the evaluation set:
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- Loss: 0.0053
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- Precision: 0.9923
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# py312-pylingual-v1-segmenter
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This model is a fine-tuned version of [syssec-utd/py312-pylingual-v1-mlm](https://huggingface.co/syssec-utd/py312-pylingual-v1-mlm) on the syssec-utd/segmentation-py312-pylingual-v1-tokenized dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0053
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- Precision: 0.9923
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