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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: kykim0/pythia-1b-tulu-v2-mix-nos |
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tags: |
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- trl |
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- reward-trainer |
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- generated_from_trainer |
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datasets: |
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- allenai/ultrafeedback_binarized_cleaned |
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metrics: |
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- accuracy |
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model-index: |
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- name: pythia-1b-tulu-v2-mix-nos-rm |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: allenai/ultrafeedback_binarized_cleaned |
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type: allenai/ultrafeedback_binarized_cleaned |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7457800511508952 |
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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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# pythia-1b-tulu-v2-mix-nos-rm |
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This model is a fine-tuned version of [kykim0/pythia-1b-tulu-v2-mix-nos](https://huggingface.co/kykim0/pythia-1b-tulu-v2-mix-nos) on the allenai/ultrafeedback_binarized_cleaned dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5227 |
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- Accuracy: 0.7458 |
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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: 1.41e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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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: 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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- num_epochs: 1 |
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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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| 0.5749 | 0.0527 | 100 | 0.5849 | 0.6900 | |
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| 0.5873 | 0.1055 | 200 | 0.5581 | 0.7100 | |
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| 0.5599 | 0.1582 | 300 | 0.5470 | 0.7212 | |
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| 0.5456 | 0.2109 | 400 | 0.5379 | 0.7258 | |
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| 0.521 | 0.2637 | 500 | 0.5358 | 0.7294 | |
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| 0.5361 | 0.3164 | 600 | 0.5363 | 0.7376 | |
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| 0.5662 | 0.3691 | 700 | 0.5270 | 0.7412 | |
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| 0.5301 | 0.4219 | 800 | 0.5268 | 0.7427 | |
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| 0.5661 | 0.4746 | 900 | 0.5301 | 0.7381 | |
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| 0.5608 | 0.5274 | 1000 | 0.5242 | 0.7437 | |
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| 0.5223 | 0.5801 | 1100 | 0.5242 | 0.7422 | |
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| 0.5322 | 0.6328 | 1200 | 0.5249 | 0.7448 | |
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| 0.4891 | 0.6856 | 1300 | 0.5241 | 0.7427 | |
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| 0.5111 | 0.7383 | 1400 | 0.5234 | 0.7437 | |
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| 0.5145 | 0.7910 | 1500 | 0.5225 | 0.7422 | |
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| 0.4746 | 0.8438 | 1600 | 0.5226 | 0.7458 | |
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| 0.5551 | 0.8965 | 1700 | 0.5223 | 0.7448 | |
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| 0.563 | 0.9492 | 1800 | 0.5222 | 0.7453 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.19.1 |
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