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--- |
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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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model-index: |
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- name: lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_lora2 |
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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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# lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_lora2 |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4374 |
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- Accuracy: 0.6510 |
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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: 0.0001 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.168 | 1.0 | 187 | 1.1156 | 0.6787 | |
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| 1.0838 | 2.0 | 375 | 1.1152 | 0.6790 | |
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| 0.9936 | 3.0 | 562 | 1.1289 | 0.6781 | |
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| 0.8717 | 4.0 | 750 | 1.1731 | 0.6742 | |
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| 0.7555 | 5.0 | 937 | 1.2357 | 0.6711 | |
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| 0.6292 | 6.0 | 1125 | 1.3174 | 0.6674 | |
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| 0.5073 | 7.0 | 1312 | 1.4164 | 0.6638 | |
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| 0.4021 | 8.0 | 1500 | 1.4974 | 0.6621 | |
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| 0.3017 | 9.0 | 1687 | 1.6756 | 0.6576 | |
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| 0.2292 | 10.0 | 1875 | 1.8038 | 0.6548 | |
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| 0.1766 | 11.0 | 2062 | 1.9233 | 0.6539 | |
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| 0.1406 | 12.0 | 2250 | 2.0465 | 0.6519 | |
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| 0.1146 | 13.0 | 2437 | 2.1205 | 0.6518 | |
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| 0.0988 | 14.0 | 2625 | 2.1974 | 0.6523 | |
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| 0.0915 | 15.0 | 2812 | 2.2592 | 0.6519 | |
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| 0.0859 | 16.0 | 3000 | 2.3234 | 0.6515 | |
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| 0.0785 | 17.0 | 3187 | 2.3664 | 0.6515 | |
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| 0.0761 | 18.0 | 3375 | 2.3999 | 0.6507 | |
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| 0.0767 | 19.0 | 3562 | 2.4329 | 0.6517 | |
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| 0.0756 | 19.95 | 3740 | 2.4374 | 0.6510 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.14.1 |
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