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--- |
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base_model: AIRI-Institute/gena-lm-bigbird-base-t2t |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: test_run |
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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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# test_run |
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This model is a fine-tuned version of [AIRI-Institute/gena-lm-bigbird-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bigbird-base-t2t) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2397 |
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- F1: 0.8195 |
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- Mcc Score: 0.5808 |
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- Accuracy: 0.7933 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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: constant_with_warmup |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Mcc Score | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:| |
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| 0.6329 | 1.0 | 94 | 0.5532 | 0.7711 | 0.4531 | 0.7335 | |
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| 0.4921 | 2.0 | 188 | 0.4789 | 0.8359 | 0.5501 | 0.7832 | |
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| 0.3981 | 3.0 | 282 | 0.4760 | 0.8347 | 0.5789 | 0.7987 | |
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| 0.3579 | 4.0 | 376 | 0.6767 | 0.7737 | 0.5377 | 0.7587 | |
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| 0.2488 | 5.0 | 470 | 0.5478 | 0.8327 | 0.5887 | 0.8015 | |
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| 0.1889 | 6.0 | 564 | 0.7844 | 0.8231 | 0.5846 | 0.7962 | |
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| 0.1569 | 7.0 | 658 | 0.8773 | 0.8254 | 0.5868 | 0.7978 | |
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| 0.1034 | 8.0 | 752 | 1.4445 | 0.7499 | 0.4939 | 0.7353 | |
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| 0.0832 | 9.0 | 846 | 1.6405 | 0.7195 | 0.4955 | 0.7205 | |
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| 0.1051 | 10.0 | 940 | 1.2397 | 0.8195 | 0.5808 | 0.7933 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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