Instructions to use MikeGreen2710/training_with_callbacks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MikeGreen2710/training_with_callbacks with Transformers:
# Load model directly from transformers import AutoTokenizer, CustomSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MikeGreen2710/training_with_callbacks") model = CustomSequenceClassification.from_pretrained("MikeGreen2710/training_with_callbacks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MikeGreen2710/model_location_with_extra_14_features
Browse files- README.md +22 -22
- model.safetensors +2 -2
- training_args.bin +1 -1
README.md
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ommlops/huggingface/runs/
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# training_with_callbacks
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This model is a fine-tuned version of [MikeGreen2710/mlm_listing_1.79](https://huggingface.co/MikeGreen2710/mlm_listing_1.79) 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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- F1: 0.9208
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## Model description
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### Framework versions
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ommlops/huggingface/runs/pktpgp7v)
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# training_with_callbacks
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This model is a fine-tuned version of [MikeGreen2710/mlm_listing_1.79](https://huggingface.co/MikeGreen2710/mlm_listing_1.79) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7291
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- F1: 0.9208
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| 0.3171 | 1.0 | 450 | 0.2395 | 0.9020 |
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| 0.1889 | 2.0 | 900 | 0.2344 | 0.9135 |
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| 0.1356 | 3.0 | 1350 | 0.3104 | 0.8986 |
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| 0.0919 | 4.0 | 1800 | 0.3622 | 0.9101 |
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| 0.0553 | 5.0 | 2250 | 0.4539 | 0.9085 |
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| 0.0347 | 6.0 | 2700 | 0.4787 | 0.9132 |
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| 0.0221 | 7.0 | 3150 | 0.5269 | 0.9084 |
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| 0.0113 | 8.0 | 3600 | 0.5861 | 0.9150 |
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| 0.016 | 9.0 | 4050 | 0.6784 | 0.9098 |
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| 0.0079 | 10.0 | 4500 | 0.6497 | 0.9200 |
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| 0.005 | 11.0 | 4950 | 0.7084 | 0.9140 |
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| 0.0074 | 12.0 | 5400 | 0.7291 | 0.9208 |
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| 0.0061 | 13.0 | 5850 | 0.7240 | 0.9174 |
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| 0.0053 | 14.0 | 6300 | 0.7493 | 0.9149 |
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| 0.004 | 15.0 | 6750 | 0.8256 | 0.9106 |
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| 0.0035 | 16.0 | 7200 | 0.7821 | 0.9158 |
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| 0.0054 | 17.0 | 7650 | 0.8016 | 0.9148 |
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| 0.0005 | 18.0 | 8100 | 0.8021 | 0.9166 |
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| 0.0012 | 19.0 | 8550 | 0.8046 | 0.9182 |
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| 0.002 | 20.0 | 9000 | 0.8045 | 0.9191 |
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### Framework versions
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training_args.bin
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