text_summarization-finetuned_cnn_dailymail
This model is a fine-tuned version of Falconsai/text_summarization on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 2.0045
- Rouge1: 0.2361
- Rouge2: 0.11
- Rougel: 0.192
- Rougelsum: 0.2212
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 10.8721 | 0.99 | 62 | 8.1409 | 0.2058 | 0.0891 | 0.1673 | 0.1924 |
| 6.0137 | 2.0 | 125 | 4.2590 | 0.1997 | 0.082 | 0.1581 | 0.188 |
| 3.7261 | 2.99 | 187 | 3.0481 | 0.2196 | 0.0942 | 0.178 | 0.2066 |
| 3.3164 | 4.0 | 250 | 2.9085 | 0.2281 | 0.103 | 0.1852 | 0.2148 |
| 3.1784 | 4.99 | 312 | 2.7974 | 0.2282 | 0.1057 | 0.1869 | 0.2155 |
| 3.0345 | 6.0 | 375 | 2.6655 | 0.2318 | 0.1084 | 0.189 | 0.2177 |
| 2.8946 | 6.99 | 437 | 2.5411 | 0.2332 | 0.1095 | 0.1906 | 0.2193 |
| 2.7696 | 8.0 | 500 | 2.4400 | 0.2333 | 0.111 | 0.1916 | 0.22 |
| 2.684 | 8.99 | 562 | 2.3651 | 0.2342 | 0.11 | 0.1924 | 0.2204 |
| 2.6073 | 10.0 | 625 | 2.3010 | 0.2344 | 0.111 | 0.1922 | 0.2205 |
| 2.5517 | 10.99 | 687 | 2.2522 | 0.2346 | 0.1108 | 0.1925 | 0.2207 |
| 2.4845 | 12.0 | 750 | 2.2108 | 0.2327 | 0.1098 | 0.1916 | 0.2186 |
| 2.4484 | 12.99 | 812 | 2.1788 | 0.2329 | 0.1098 | 0.1922 | 0.2187 |
| 2.4194 | 14.0 | 875 | 2.1517 | 0.2336 | 0.1087 | 0.1919 | 0.2188 |
| 2.3908 | 14.99 | 937 | 2.1290 | 0.2343 | 0.109 | 0.1918 | 0.2195 |
| 2.3657 | 16.0 | 1000 | 2.1060 | 0.2324 | 0.107 | 0.1895 | 0.2175 |
| 2.3215 | 16.99 | 1062 | 2.0887 | 0.232 | 0.1066 | 0.1895 | 0.2171 |
| 2.3236 | 18.0 | 1125 | 2.0746 | 0.2328 | 0.1075 | 0.1899 | 0.2181 |
| 2.3018 | 18.99 | 1187 | 2.0612 | 0.2337 | 0.1067 | 0.1898 | 0.2183 |
| 2.2788 | 20.0 | 1250 | 2.0500 | 0.2337 | 0.1071 | 0.1901 | 0.2187 |
| 2.2502 | 20.99 | 1312 | 2.0406 | 0.2338 | 0.1072 | 0.1897 | 0.2187 |
| 2.2652 | 22.0 | 1375 | 2.0317 | 0.2339 | 0.1072 | 0.1898 | 0.2188 |
| 2.2508 | 22.99 | 1437 | 2.0253 | 0.2332 | 0.1069 | 0.1891 | 0.2181 |
| 2.2233 | 24.0 | 1500 | 2.0192 | 0.235 | 0.1087 | 0.1908 | 0.2202 |
| 2.2225 | 24.99 | 1562 | 2.0144 | 0.2352 | 0.1095 | 0.1912 | 0.2202 |
| 2.2248 | 26.0 | 1625 | 2.0107 | 0.2353 | 0.1094 | 0.1915 | 0.2204 |
| 2.235 | 26.99 | 1687 | 2.0075 | 0.235 | 0.1092 | 0.1915 | 0.2201 |
| 2.1964 | 28.0 | 1750 | 2.0056 | 0.2359 | 0.1096 | 0.1917 | 0.2209 |
| 2.1996 | 28.99 | 1812 | 2.0047 | 0.2361 | 0.11 | 0.192 | 0.2212 |
| 2.2228 | 29.76 | 1860 | 2.0045 | 0.2361 | 0.11 | 0.192 | 0.2212 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1