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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: 0.5622 |
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- Loc: {'precision': 0.9222857142857143, 'recall': 0.9449648711943794, 'f1': 0.9334875650665124, 'number': 854} |
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- Org: {'precision': 0.8973561430793157, 'recall': 0.8876923076923077, 'f1': 0.8924980665119876, 'number': 650} |
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- Per: {'precision': 0.9014373716632443, 'recall': 0.9440860215053763, 'f1': 0.9222689075630252, 'number': 465} |
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- Overall Precision: 0.9092 |
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- Overall Recall: 0.9259 |
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- Overall F1: 0.9175 |
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- Overall Accuracy: 0.9582 |
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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: 20 |
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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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| 0.1729 | 10.0 | 5000 | 0.4248 | {'precision': 0.9111361079865017, 'recall': 0.9484777517564403, 'f1': 0.9294320137693631, 'number': 854} | {'precision': 0.9027113237639554, 'recall': 0.8707692307692307, 'f1': 0.8864526233359435, 'number': 650} | {'precision': 0.9010309278350516, 'recall': 0.9397849462365592, 'f1': 0.92, 'number': 465} | 0.9060 | 0.9208 | 0.9134 | 0.9584 | |
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| 0.0068 | 20.0 | 10000 | 0.5622 | {'precision': 0.9222857142857143, 'recall': 0.9449648711943794, 'f1': 0.9334875650665124, 'number': 854} | {'precision': 0.8973561430793157, 'recall': 0.8876923076923077, 'f1': 0.8924980665119876, 'number': 650} | {'precision': 0.9014373716632443, 'recall': 0.9440860215053763, 'f1': 0.9222689075630252, 'number': 465} | 0.9092 | 0.9259 | 0.9175 | 0.9582 | |
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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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