Instructions to use bolu61/logsegmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bolu61/logsegmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bolu61/logsegmenter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bolu61/logsegmenter") model = AutoModelForTokenClassification.from_pretrained("bolu61/logsegmenter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2963
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 1.076 | 1.0 | 807 | 0.7889 |
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| 0.9412 | 2.0 | 1614 | 0.5343 |
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| 0.5653 | 3.0 | 2421 | 0.4825 |
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| 0.6135 | 4.0 | 3228 | 0.4739 |
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| 0.5656 | 5.0 | 4035 | 0.4182 |
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| 0.3505 | 6.0 | 4842 | 0.3435 |
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| 0.1471 | 7.0 | 5649 | 0.3291 |
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| 0.3406 | 8.0 | 6456 | 0.3240 |
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| 0.2795 | 9.0 | 7263 | 0.3131 |
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| 0.1624 | 10.0 | 8070 | 0.2963 |
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### Framework versions
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