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--- |
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license: apache-2.0 |
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base_model: google/mt5-large |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: results |
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results: [] |
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--- |
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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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# results |
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This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9627 |
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- Loc: {'precision': 0.07002967359050445, 'recall': 0.13817330210772832, 'f1': 0.09294998030720757, 'number': 854} |
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- Org: {'precision': 0.06141439205955335, 'recall': 0.1523076923076923, 'f1': 0.08753315649867373, 'number': 650} |
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- Per: {'precision': 0.030874785591766724, 'recall': 0.07741935483870968, 'f1': 0.04414469650521153, 'number': 465} |
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- Overall Precision: 0.0567 |
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- Overall Recall: 0.1285 |
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- Overall F1: 0.0787 |
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- Overall Accuracy: 0.3287 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Loc | Org | Per | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 3.8187 | 2.0 | 10 | 3.1219 | {'precision': 0.06360022714366836, 'recall': 0.13114754098360656, 'f1': 0.08565965583173997, 'number': 854} | {'precision': 0.05763688760806916, 'recall': 0.15384615384615385, 'f1': 0.08385744234800839, 'number': 650} | {'precision': 0.027879677182685254, 'recall': 0.08172043010752689, 'f1': 0.04157549234135668, 'number': 465} | 0.0515 | 0.1270 | 0.0732 | 0.2983 | |
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| 3.2942 | 4.0 | 20 | 2.9627 | {'precision': 0.07002967359050445, 'recall': 0.13817330210772832, 'f1': 0.09294998030720757, 'number': 854} | {'precision': 0.06141439205955335, 'recall': 0.1523076923076923, 'f1': 0.08753315649867373, 'number': 650} | {'precision': 0.030874785591766724, 'recall': 0.07741935483870968, 'f1': 0.04414469650521153, 'number': 465} | 0.0567 | 0.1285 | 0.0787 | 0.3287 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 1.11.0a0+17540c5 |
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- Datasets 2.20.0 |
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- Tokenizers 0.15.2 |
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