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--- |
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base_model: AIRI-Institute/gena-lm-bert-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: gut_1024-finetuned-lora-bert-base-t2t |
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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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# gut_1024-finetuned-lora-bert-base-t2t |
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This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4700 |
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- F1: 0.8448 |
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- Mcc Score: 0.5728 |
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- Accuracy: 0.7943 |
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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: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 64 |
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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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- training_steps: 1000 |
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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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| 0.6922 | 0.02 | 100 | 0.6644 | 0.7478 | 0.0 | 0.5971 | |
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| 0.6582 | 0.04 | 200 | 0.6766 | 0.4699 | 0.2578 | 0.5570 | |
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| 0.6578 | 0.05 | 300 | 0.5801 | 0.8210 | 0.4886 | 0.7508 | |
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| 0.5793 | 0.07 | 400 | 0.5814 | 0.8013 | 0.4141 | 0.7082 | |
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| 0.5933 | 0.09 | 500 | 0.5877 | 0.7408 | 0.4494 | 0.7183 | |
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| 0.5616 | 0.11 | 600 | 0.5000 | 0.8229 | 0.5282 | 0.7766 | |
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| 0.5168 | 0.12 | 700 | 0.5027 | 0.8347 | 0.5540 | 0.7884 | |
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| 0.4788 | 0.14 | 800 | 0.5284 | 0.7922 | 0.5012 | 0.7572 | |
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| 0.5255 | 0.16 | 900 | 0.4859 | 0.8445 | 0.5696 | 0.7901 | |
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| 0.5404 | 0.18 | 1000 | 0.4700 | 0.8448 | 0.5728 | 0.7943 | |
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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 2.17.1 |
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- Tokenizers 0.15.2 |
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