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flan-t5-xl-absa-multitask-rest
Shakhovak/flan-t5-xl-absa-multitask-rest
flan-t5-xl-absa-multitask-rest is a machine learning model from Shakhovak. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Apr 11, 2024
Repo size
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At a glance
- Access
- Public
- Created
- Apr 10, 2024
- Updated
- Apr 11, 2024
- SHA
- b92e4234
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
flan-t5-xl-absa-multitask-rest
This model is a fine-tuned version of ybelkada/flan-t5-xl-sharded-bf16 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1127
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.3549 | 0.32 | 200 | 3.5848 |
| 1.5908 | 0.63 | 400 | 0.5331 |
| 0.4981 | 0.95 | 600 | 0.3159 |
| 0.351 | 1.27 | 800 | 0.2457 |
| 0.2884 | 1.58 | 1000 | 0.2118 |
| 0.2592 | 1.9 | 1200 | 0.2000 |
| 0.2323 | 2.22 | 1400 | 0.1839 |
| 0.2107 | 2.53 | 1600 | 0.1704 |
| 0.2071 | 2.85 | 1800 | 0.1649 |
| 0.1944 | 3.16 | 2000 | 0.1634 |
| 0.1774 | 3.48 | 2200 | 0.1549 |
| 0.1796 | 3.8 | 2400 | 0.1505 |
| 0.1695 | 4.11 | 2600 | 0.1427 |
| 0.1569 | 4.43 | 2800 | 0.1403 |
| 0.1662 | 4.75 | 3000 | 0.1395 |
| 0.15 | 5.06 | 3200 | 0.1351 |
| 0.1448 | 5.38 | 3400 | 0.1283 |
| 0.1444 | 5.7 | 3600 | 0.1302 |
| 0.1506 | 6.01 | 3800 | 0.1237 |
| 0.1321 | 6.33 | 4000 | 0.1264 |
| 0.1318 | 6.65 | 4200 | 0.1269 |
| 0.1298 | 6.96 | 4400 | 0.1207 |
| 0.1273 | 7.28 | 4600 | 0.1224 |
| 0.123 | 7.59 | 4800 | 0.1209 |
| 0.1278 | 7.91 | 5000 | 0.1222 |
| 0.1236 | 8.23 | 5200 | 0.1165 |
| 0.1188 | 8.54 | 5400 | 0.1154 |
| 0.1181 | 8.86 | 5600 | 0.1173 |
| 0.1126 | 9.18 | 5800 | 0.1177 |
| 0.113 | 9.49 | 6000 | 0.1194 |
| 0.1086 | 9.81 | 6200 | 0.1148 |
| 0.1086 | 10.13 | 6400 | 0.1158 |
| 0.1118 | 10.44 | 6600 | 0.1145 |
| 0.105 | 10.76 | 6800 | 0.1125 |
| 0.1119 | 11.08 | 7000 | 0.1146 |
| 0.1007 | 11.39 | 7200 | 0.1123 |
| 0.114 | 11.71 | 7400 | 0.1127 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Created
- Apr 10, 2024
- Updated
- Apr 11, 2024