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--- |
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base_model: ybelkada/flan-t5-xl-sharded-bf16 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: flan-t5-xl-absa-multitask-rest |
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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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# flan-t5-xl-absa-multitask-rest |
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This model is a fine-tuned version of [ybelkada/flan-t5-xl-sharded-bf16](https://huggingface.co/ybelkada/flan-t5-xl-sharded-bf16) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1127 |
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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: 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: 0.1 |
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- num_epochs: 12 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 4.3549 | 0.32 | 200 | 3.5848 | |
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| 1.5908 | 0.63 | 400 | 0.5331 | |
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| 0.4981 | 0.95 | 600 | 0.3159 | |
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| 0.351 | 1.27 | 800 | 0.2457 | |
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| 0.2884 | 1.58 | 1000 | 0.2118 | |
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| 0.2592 | 1.9 | 1200 | 0.2000 | |
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| 0.2323 | 2.22 | 1400 | 0.1839 | |
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| 0.2107 | 2.53 | 1600 | 0.1704 | |
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| 0.2071 | 2.85 | 1800 | 0.1649 | |
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| 0.1944 | 3.16 | 2000 | 0.1634 | |
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| 0.1774 | 3.48 | 2200 | 0.1549 | |
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| 0.1796 | 3.8 | 2400 | 0.1505 | |
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| 0.1695 | 4.11 | 2600 | 0.1427 | |
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| 0.1569 | 4.43 | 2800 | 0.1403 | |
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| 0.1662 | 4.75 | 3000 | 0.1395 | |
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| 0.15 | 5.06 | 3200 | 0.1351 | |
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| 0.1448 | 5.38 | 3400 | 0.1283 | |
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| 0.1444 | 5.7 | 3600 | 0.1302 | |
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| 0.1506 | 6.01 | 3800 | 0.1237 | |
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| 0.1321 | 6.33 | 4000 | 0.1264 | |
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| 0.1318 | 6.65 | 4200 | 0.1269 | |
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| 0.1298 | 6.96 | 4400 | 0.1207 | |
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| 0.1273 | 7.28 | 4600 | 0.1224 | |
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| 0.123 | 7.59 | 4800 | 0.1209 | |
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| 0.1278 | 7.91 | 5000 | 0.1222 | |
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| 0.1236 | 8.23 | 5200 | 0.1165 | |
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| 0.1188 | 8.54 | 5400 | 0.1154 | |
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| 0.1181 | 8.86 | 5600 | 0.1173 | |
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| 0.1126 | 9.18 | 5800 | 0.1177 | |
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| 0.113 | 9.49 | 6000 | 0.1194 | |
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| 0.1086 | 9.81 | 6200 | 0.1148 | |
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| 0.1086 | 10.13 | 6400 | 0.1158 | |
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| 0.1118 | 10.44 | 6600 | 0.1145 | |
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| 0.105 | 10.76 | 6800 | 0.1125 | |
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| 0.1119 | 11.08 | 7000 | 0.1146 | |
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| 0.1007 | 11.39 | 7200 | 0.1123 | |
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| 0.114 | 11.71 | 7400 | 0.1127 | |
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### Framework versions |
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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 |
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