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--- |
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license: apache-2.0 |
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base_model: google/flan-t5-small |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: medication-lists |
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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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# medication-lists |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1133 |
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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: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.6316 | 0.46 | 50 | 0.6287 | |
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| 0.6403 | 0.93 | 100 | 0.3378 | |
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| 0.4213 | 1.39 | 150 | 0.2460 | |
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| 0.3452 | 1.85 | 200 | 0.2184 | |
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| 0.306 | 2.31 | 250 | 0.1903 | |
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| 0.2634 | 2.78 | 300 | 0.1807 | |
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| 0.2423 | 3.24 | 350 | 0.1630 | |
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| 0.2224 | 3.7 | 400 | 0.1599 | |
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| 0.2107 | 4.17 | 450 | 0.1522 | |
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| 0.1922 | 4.63 | 500 | 0.1515 | |
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| 0.1887 | 5.09 | 550 | 0.1394 | |
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| 0.1821 | 5.56 | 600 | 0.1414 | |
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| 0.1705 | 6.02 | 650 | 0.1378 | |
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| 0.1602 | 6.48 | 700 | 0.1330 | |
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| 0.1579 | 6.94 | 750 | 0.1300 | |
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| 0.1497 | 7.41 | 800 | 0.1282 | |
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| 0.1534 | 7.87 | 850 | 0.1277 | |
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| 0.147 | 8.33 | 900 | 0.1274 | |
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| 0.1395 | 8.8 | 950 | 0.1204 | |
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| 0.1361 | 9.26 | 1000 | 0.1235 | |
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| 0.1353 | 9.72 | 1050 | 0.1210 | |
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| 0.1303 | 10.19 | 1100 | 0.1220 | |
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| 0.132 | 10.65 | 1150 | 0.1232 | |
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| 0.1262 | 11.11 | 1200 | 0.1193 | |
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| 0.1228 | 11.57 | 1250 | 0.1229 | |
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| 0.1261 | 12.04 | 1300 | 0.1215 | |
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| 0.1204 | 12.5 | 1350 | 0.1163 | |
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| 0.12 | 12.96 | 1400 | 0.1189 | |
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| 0.11 | 13.43 | 1450 | 0.1173 | |
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| 0.1183 | 13.89 | 1500 | 0.1149 | |
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| 0.108 | 14.35 | 1550 | 0.1178 | |
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| 0.1122 | 14.81 | 1600 | 0.1150 | |
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| 0.1126 | 15.28 | 1650 | 0.1157 | |
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| 0.112 | 15.74 | 1700 | 0.1152 | |
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| 0.1046 | 16.2 | 1750 | 0.1156 | |
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| 0.1057 | 16.67 | 1800 | 0.1138 | |
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| 0.1067 | 17.13 | 1850 | 0.1129 | |
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| 0.1078 | 17.59 | 1900 | 0.1140 | |
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| 0.1043 | 18.06 | 1950 | 0.1135 | |
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| 0.1033 | 18.52 | 2000 | 0.1138 | |
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| 0.1017 | 18.98 | 2050 | 0.1140 | |
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| 0.102 | 19.44 | 2100 | 0.1125 | |
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| 0.1012 | 19.91 | 2150 | 0.1133 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.11.0 |
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- Tokenizers 0.14.1 |
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