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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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- wer |
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base_model: google/mt5-small |
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model-index: |
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- name: nep-spell-mt5-small-0 |
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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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# nep-spell-mt5-small-0 |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0009 |
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- Accuracy: 0.8446 |
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- Precision: 0.889 |
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- Recall: 0.8446 |
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- F1: 0.8603 |
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- Wer: 0.0105 |
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- Cer: 0.0024 |
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- Chrf: 99.2925 |
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- Exact Match: 0.8446 |
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- Bertscore:precision: 0.9972 |
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- Bertscore:recall: 0.9975 |
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- Bertscore:f1: 0.9973 |
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- Ter: 1.0546 |
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- Blerurt: 0.8889 |
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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: 3 |
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- eval_batch_size: 3 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Wer | Cer | Chrf | Exact Match | Bertscore:precision | Bertscore:recall | Bertscore:f1 | Ter | Blerurt | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|:-------:|:-----------:|:-------------------:|:----------------:|:------------:|:------:|:-------:| |
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| 0.006 | 0.75 | 10000 | 0.0009 | 0.8446 | 0.889 | 0.8446 | 0.8603 | 0.0105 | 0.0024 | 99.2925 | 0.8446 | 0.9972 | 0.9975 | 0.9973 | 1.0546 | 0.8889 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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