t5-small-entailement-Writer
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5958
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 42 | 1.8511 |
| No log | 2.0 | 84 | 1.2249 |
| No log | 3.0 | 126 | 0.9976 |
| No log | 4.0 | 168 | 0.9108 |
| No log | 5.0 | 210 | 0.8478 |
| No log | 6.0 | 252 | 0.8186 |
| No log | 7.0 | 294 | 0.7965 |
| No log | 8.0 | 336 | 0.7815 |
| No log | 9.0 | 378 | 0.7634 |
| No log | 10.0 | 420 | 0.7544 |
| No log | 11.0 | 462 | 0.7408 |
| 1.2198 | 12.0 | 504 | 0.7298 |
| 1.2198 | 13.0 | 546 | 0.7240 |
| 1.2198 | 14.0 | 588 | 0.7139 |
| 1.2198 | 15.0 | 630 | 0.7070 |
| 1.2198 | 16.0 | 672 | 0.7028 |
| 1.2198 | 17.0 | 714 | 0.6977 |
| 1.2198 | 18.0 | 756 | 0.6926 |
| 1.2198 | 19.0 | 798 | 0.6906 |
| 1.2198 | 20.0 | 840 | 0.6846 |
| 1.2198 | 21.0 | 882 | 0.6822 |
| 1.2198 | 22.0 | 924 | 0.6760 |
| 1.2198 | 23.0 | 966 | 0.6710 |
| 0.7403 | 24.0 | 1008 | 0.6667 |
| 0.7403 | 25.0 | 1050 | 0.6657 |
| 0.7403 | 26.0 | 1092 | 0.6653 |
| 0.7403 | 27.0 | 1134 | 0.6588 |
| 0.7403 | 28.0 | 1176 | 0.6584 |
| 0.7403 | 29.0 | 1218 | 0.6573 |
| 0.7403 | 30.0 | 1260 | 0.6520 |
| 0.7403 | 31.0 | 1302 | 0.6522 |
| 0.7403 | 32.0 | 1344 | 0.6525 |
| 0.7403 | 33.0 | 1386 | 0.6463 |
| 0.7403 | 34.0 | 1428 | 0.6453 |
| 0.7403 | 35.0 | 1470 | 0.6437 |
| 0.6642 | 36.0 | 1512 | 0.6397 |
| 0.6642 | 37.0 | 1554 | 0.6382 |
| 0.6642 | 38.0 | 1596 | 0.6365 |
| 0.6642 | 39.0 | 1638 | 0.6332 |
| 0.6642 | 40.0 | 1680 | 0.6335 |
| 0.6642 | 41.0 | 1722 | 0.6325 |
| 0.6642 | 42.0 | 1764 | 0.6295 |
| 0.6642 | 43.0 | 1806 | 0.6304 |
| 0.6642 | 44.0 | 1848 | 0.6287 |
| 0.6642 | 45.0 | 1890 | 0.6272 |
| 0.6642 | 46.0 | 1932 | 0.6267 |
| 0.6642 | 47.0 | 1974 | 0.6242 |
| 0.6127 | 48.0 | 2016 | 0.6232 |
| 0.6127 | 49.0 | 2058 | 0.6225 |
| 0.6127 | 50.0 | 2100 | 0.6211 |
| 0.6127 | 51.0 | 2142 | 0.6204 |
| 0.6127 | 52.0 | 2184 | 0.6196 |
| 0.6127 | 53.0 | 2226 | 0.6183 |
| 0.6127 | 54.0 | 2268 | 0.6168 |
| 0.6127 | 55.0 | 2310 | 0.6175 |
| 0.6127 | 56.0 | 2352 | 0.6160 |
| 0.6127 | 57.0 | 2394 | 0.6154 |
| 0.6127 | 58.0 | 2436 | 0.6143 |
| 0.6127 | 59.0 | 2478 | 0.6142 |
| 0.5799 | 60.0 | 2520 | 0.6131 |
| 0.5799 | 61.0 | 2562 | 0.6122 |
| 0.5799 | 62.0 | 2604 | 0.6120 |
| 0.5799 | 63.0 | 2646 | 0.6115 |
| 0.5799 | 64.0 | 2688 | 0.6119 |
| 0.5799 | 65.0 | 2730 | 0.6112 |
| 0.5799 | 66.0 | 2772 | 0.6099 |
| 0.5799 | 67.0 | 2814 | 0.6094 |
| 0.5799 | 68.0 | 2856 | 0.6082 |
| 0.5799 | 69.0 | 2898 | 0.6092 |
| 0.5799 | 70.0 | 2940 | 0.6081 |
| 0.5799 | 71.0 | 2982 | 0.6071 |
| 0.5558 | 72.0 | 3024 | 0.6062 |
| 0.5558 | 73.0 | 3066 | 0.6079 |
| 0.5558 | 74.0 | 3108 | 0.6072 |
| 0.5558 | 75.0 | 3150 | 0.6052 |
| 0.5558 | 76.0 | 3192 | 0.6066 |
| 0.5558 | 77.0 | 3234 | 0.6049 |
| 0.5558 | 78.0 | 3276 | 0.6042 |
| 0.5558 | 79.0 | 3318 | 0.6039 |
| 0.5558 | 80.0 | 3360 | 0.6050 |
| 0.5558 | 81.0 | 3402 | 0.6042 |
| 0.5558 | 82.0 | 3444 | 0.6040 |
| 0.5558 | 83.0 | 3486 | 0.6029 |
| 0.5292 | 84.0 | 3528 | 0.6032 |
| 0.5292 | 85.0 | 3570 | 0.6039 |
| 0.5292 | 86.0 | 3612 | 0.6036 |
| 0.5292 | 87.0 | 3654 | 0.6019 |
| 0.5292 | 88.0 | 3696 | 0.6014 |
| 0.5292 | 89.0 | 3738 | 0.6022 |
| 0.5292 | 90.0 | 3780 | 0.6014 |
| 0.5292 | 91.0 | 3822 | 0.6020 |
| 0.5292 | 92.0 | 3864 | 0.6028 |
| 0.5292 | 93.0 | 3906 | 0.5994 |
| 0.5292 | 94.0 | 3948 | 0.6004 |
| 0.5292 | 95.0 | 3990 | 0.5987 |
| 0.5159 | 96.0 | 4032 | 0.5992 |
| 0.5159 | 97.0 | 4074 | 0.5993 |
| 0.5159 | 98.0 | 4116 | 0.5989 |
| 0.5159 | 99.0 | 4158 | 0.6004 |
| 0.5159 | 100.0 | 4200 | 0.6001 |
| 0.5159 | 101.0 | 4242 | 0.6008 |
| 0.5159 | 102.0 | 4284 | 0.6006 |
| 0.5159 | 103.0 | 4326 | 0.5999 |
| 0.5159 | 104.0 | 4368 | 0.5994 |
| 0.5159 | 105.0 | 4410 | 0.5996 |
| 0.5159 | 106.0 | 4452 | 0.5991 |
| 0.5159 | 107.0 | 4494 | 0.5990 |
| 0.5004 | 108.0 | 4536 | 0.5996 |
| 0.5004 | 109.0 | 4578 | 0.5988 |
| 0.5004 | 110.0 | 4620 | 0.5992 |
| 0.5004 | 111.0 | 4662 | 0.5984 |
| 0.5004 | 112.0 | 4704 | 0.5982 |
| 0.5004 | 113.0 | 4746 | 0.5973 |
| 0.5004 | 114.0 | 4788 | 0.5984 |
| 0.5004 | 115.0 | 4830 | 0.5973 |
| 0.5004 | 116.0 | 4872 | 0.5977 |
| 0.5004 | 117.0 | 4914 | 0.5970 |
| 0.5004 | 118.0 | 4956 | 0.5976 |
| 0.5004 | 119.0 | 4998 | 0.5962 |
| 0.488 | 120.0 | 5040 | 0.5969 |
| 0.488 | 121.0 | 5082 | 0.5965 |
| 0.488 | 122.0 | 5124 | 0.5969 |
| 0.488 | 123.0 | 5166 | 0.5972 |
| 0.488 | 124.0 | 5208 | 0.5966 |
| 0.488 | 125.0 | 5250 | 0.5962 |
| 0.488 | 126.0 | 5292 | 0.5966 |
| 0.488 | 127.0 | 5334 | 0.5960 |
| 0.488 | 128.0 | 5376 | 0.5969 |
| 0.488 | 129.0 | 5418 | 0.5960 |
| 0.488 | 130.0 | 5460 | 0.5960 |
| 0.483 | 131.0 | 5502 | 0.5960 |
| 0.483 | 132.0 | 5544 | 0.5965 |
| 0.483 | 133.0 | 5586 | 0.5965 |
| 0.483 | 134.0 | 5628 | 0.5963 |
| 0.483 | 135.0 | 5670 | 0.5965 |
| 0.483 | 136.0 | 5712 | 0.5962 |
| 0.483 | 137.0 | 5754 | 0.5963 |
| 0.483 | 138.0 | 5796 | 0.5961 |
| 0.483 | 139.0 | 5838 | 0.5963 |
| 0.483 | 140.0 | 5880 | 0.5964 |
| 0.483 | 141.0 | 5922 | 0.5957 |
| 0.483 | 142.0 | 5964 | 0.5957 |
| 0.4809 | 143.0 | 6006 | 0.5957 |
| 0.4809 | 144.0 | 6048 | 0.5956 |
| 0.4809 | 145.0 | 6090 | 0.5958 |
| 0.4809 | 146.0 | 6132 | 0.5958 |
| 0.4809 | 147.0 | 6174 | 0.5959 |
| 0.4809 | 148.0 | 6216 | 0.5958 |
| 0.4809 | 149.0 | 6258 | 0.5958 |
| 0.4809 | 150.0 | 6300 | 0.5958 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2