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--- |
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license: other |
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base_model: facebook/opt-350m |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: opt-history-gen2 |
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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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# opt-history-gen2 |
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This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8025 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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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: 6 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 3.1245 | 0.1693 | 100 | 3.0013 | |
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| 2.9473 | 0.3386 | 200 | 2.9180 | |
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| 2.9014 | 0.5078 | 300 | 2.8787 | |
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| 2.873 | 0.6771 | 400 | 2.8646 | |
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| 2.8631 | 0.8464 | 500 | 2.8631 | |
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| 2.8366 | 1.0157 | 600 | 2.8451 | |
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| 2.64 | 1.1849 | 700 | 2.8380 | |
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| 2.6295 | 1.3542 | 800 | 2.8202 | |
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| 2.6272 | 1.5235 | 900 | 2.7987 | |
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| 2.6327 | 1.6928 | 1000 | 2.7971 | |
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| 2.626 | 1.8620 | 1100 | 2.7739 | |
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| 2.5237 | 2.0313 | 1200 | 2.7829 | |
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| 2.3242 | 2.2006 | 1300 | 2.7812 | |
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| 2.319 | 2.3699 | 1400 | 2.7727 | |
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| 2.3314 | 2.5391 | 1500 | 2.7668 | |
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| 2.3579 | 2.7084 | 1600 | 2.7561 | |
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| 2.307 | 2.8777 | 1700 | 2.7586 | |
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| 2.2612 | 3.0470 | 1800 | 2.7795 | |
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| 2.056 | 3.2163 | 1900 | 2.7801 | |
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| 2.0802 | 3.3855 | 2000 | 2.7670 | |
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| 2.1104 | 3.5548 | 2100 | 2.7708 | |
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| 2.1115 | 3.7241 | 2200 | 2.7629 | |
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| 2.0828 | 3.8934 | 2300 | 2.7606 | |
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| 1.996 | 4.0626 | 2400 | 2.7849 | |
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| 1.8701 | 4.2319 | 2500 | 2.7938 | |
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| 1.92 | 4.4012 | 2600 | 2.7928 | |
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| 1.8844 | 4.5705 | 2700 | 2.7846 | |
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| 1.9058 | 4.7397 | 2800 | 2.7840 | |
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| 1.901 | 4.9090 | 2900 | 2.7821 | |
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| 1.8418 | 5.0783 | 3000 | 2.8017 | |
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| 1.757 | 5.2476 | 3100 | 2.8055 | |
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| 1.7503 | 5.4168 | 3200 | 2.8095 | |
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| 1.7606 | 5.5861 | 3300 | 2.8065 | |
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| 1.7316 | 5.7554 | 3400 | 2.8043 | |
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| 1.7632 | 5.9247 | 3500 | 2.8025 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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