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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: ner_cs |
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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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# ner_cs |
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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: 0.5017 |
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- Loc: {'precision': 0.8522895125553914, 'recall': 0.9058084772370487, 'f1': 0.878234398782344, 'number': 637} |
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- Org: {'precision': 0.8361702127659575, 'recall': 0.8488120950323974, 'f1': 0.8424437299035369, 'number': 463} |
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- Per: {'precision': 0.9230769230769231, 'recall': 0.9737470167064439, 'f1': 0.9477351916376306, 'number': 419} |
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- Overall Precision: 0.8672 |
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- Overall Recall: 0.9072 |
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- Overall F1: 0.8867 |
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- Overall Accuracy: 0.9365 |
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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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| 0.2192 | 5.71 | 5000 | 0.2824 | {'precision': 0.8384728340675477, 'recall': 0.8963893249607535, 'f1': 0.8664643399089529, 'number': 637} | {'precision': 0.808641975308642, 'recall': 0.8488120950323974, 'f1': 0.8282402528977871, 'number': 463} | {'precision': 0.9325581395348838, 'recall': 0.9570405727923628, 'f1': 0.944640753828033, 'number': 419} | 0.8547 | 0.8986 | 0.8761 | 0.9363 | |
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| 0.0244 | 11.43 | 10000 | 0.4134 | {'precision': 0.8622754491017964, 'recall': 0.9042386185243328, 'f1': 0.8827586206896552, 'number': 637} | {'precision': 0.841991341991342, 'recall': 0.8401727861771058, 'f1': 0.8410810810810811, 'number': 463} | {'precision': 0.920814479638009, 'recall': 0.9713603818615751, 'f1': 0.9454123112659697, 'number': 419} | 0.8728 | 0.9032 | 0.8877 | 0.9370 | |
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| 0.0066 | 17.14 | 15000 | 0.5017 | {'precision': 0.8522895125553914, 'recall': 0.9058084772370487, 'f1': 0.878234398782344, 'number': 637} | {'precision': 0.8361702127659575, 'recall': 0.8488120950323974, 'f1': 0.8424437299035369, 'number': 463} | {'precision': 0.9230769230769231, 'recall': 0.9737470167064439, 'f1': 0.9477351916376306, 'number': 419} | 0.8672 | 0.9072 | 0.8867 | 0.9365 | |
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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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