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--- |
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license: cc-by-4.0 |
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library_name: peft |
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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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base_model: deepset/roberta-base-squad2 |
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model-index: |
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- name: STS-Lora-Fine-Tuning-Capstone-roberta-base-deepset-filtered-115-with-higher-r-mid |
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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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# STS-Lora-Fine-Tuning-Capstone-roberta-base-deepset-filtered-115-with-higher-r-mid |
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This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9056 |
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- Accuracy: 0.6040 |
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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: 3e-05 |
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- train_batch_size: 64 |
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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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- num_epochs: 30 |
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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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| No log | 1.0 | 76 | 1.2568 | 0.4161 | |
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| No log | 2.0 | 152 | 1.1655 | 0.4603 | |
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| No log | 3.0 | 228 | 1.0574 | 0.5120 | |
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| No log | 4.0 | 304 | 0.9846 | 0.5574 | |
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| No log | 5.0 | 380 | 0.9665 | 0.5675 | |
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| No log | 6.0 | 456 | 0.9544 | 0.5738 | |
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| 1.0456 | 7.0 | 532 | 0.9503 | 0.5763 | |
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| 1.0456 | 8.0 | 608 | 0.9269 | 0.5876 | |
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| 1.0456 | 9.0 | 684 | 0.9233 | 0.5889 | |
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| 1.0456 | 10.0 | 760 | 0.9264 | 0.5927 | |
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| 1.0456 | 11.0 | 836 | 0.9092 | 0.5927 | |
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| 1.0456 | 12.0 | 912 | 0.9187 | 0.5914 | |
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| 1.0456 | 13.0 | 988 | 0.9122 | 0.6003 | |
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| 0.8486 | 14.0 | 1064 | 0.9091 | 0.5977 | |
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| 0.8486 | 15.0 | 1140 | 0.9079 | 0.5965 | |
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| 0.8486 | 16.0 | 1216 | 0.9144 | 0.5952 | |
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| 0.8486 | 17.0 | 1292 | 0.9049 | 0.5977 | |
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| 0.8486 | 18.0 | 1368 | 0.9257 | 0.5939 | |
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| 0.8486 | 19.0 | 1444 | 0.9006 | 0.5952 | |
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| 0.8112 | 20.0 | 1520 | 0.9008 | 0.6015 | |
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| 0.8112 | 21.0 | 1596 | 0.9044 | 0.6040 | |
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| 0.8112 | 22.0 | 1672 | 0.9008 | 0.6053 | |
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| 0.8112 | 23.0 | 1748 | 0.9052 | 0.6028 | |
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| 0.8112 | 24.0 | 1824 | 0.9065 | 0.6028 | |
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| 0.8112 | 25.0 | 1900 | 0.9015 | 0.6053 | |
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| 0.8112 | 26.0 | 1976 | 0.9141 | 0.5965 | |
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| 0.7992 | 27.0 | 2052 | 0.9072 | 0.6053 | |
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| 0.7992 | 28.0 | 2128 | 0.9042 | 0.6053 | |
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| 0.7992 | 29.0 | 2204 | 0.9054 | 0.6040 | |
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| 0.7992 | 30.0 | 2280 | 0.9056 | 0.6040 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
