mt5-small_final_final_new
This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2941
- Rouge1: 41.3841
- Rouge2: 32.6198
- Rougel: 38.6245
- Rougelsum: 38.6833
- Bleu: 28.8775
- Gen Len: 17.0839
- Meteor: 0.3704
- No ans accuracy: 0.0
- Av cosine sim: 0.7627
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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 9
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | Meteor | No ans accuracy | Av cosine sim |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14.5708 | 1.0 | 175 | 4.8623 | 10.2732 | 3.6837 | 9.295 | 9.3426 | 2.4037 | 8.7507 | 0.0865 | 0.0 | 0.4429 |
| 6.5938 | 1.99 | 350 | 3.0321 | 10.3823 | 5.1376 | 9.566 | 9.6003 | 3.8998 | 7.844 | 0.0969 | 0.0 | 0.4234 |
| 4.3372 | 2.99 | 525 | 2.3227 | 26.9602 | 18.9826 | 25.2396 | 25.2665 | 9.7754 | 12.2901 | 0.2376 | 0.0 | 0.6442 |
| 3.4266 | 3.98 | 700 | 2.0083 | 31.5678 | 23.6447 | 29.6748 | 29.7026 | 12.8064 | 13.222 | 0.2877 | 0.0 | 0.6947 |
| 3.0011 | 4.98 | 875 | 1.8600 | 32.2283 | 24.3874 | 30.2293 | 30.2518 | 14.2873 | 13.6664 | 0.2984 | 0.0 | 0.704 |
| 2.7444 | 5.97 | 1050 | 1.7535 | 32.4685 | 24.6833 | 30.4294 | 30.4397 | 14.9587 | 13.8386 | 0.3029 | 0.0 | 0.7074 |
| 2.5506 | 6.97 | 1225 | 1.6692 | 32.5693 | 24.8903 | 30.5541 | 30.5742 | 15.3203 | 13.9335 | 0.305 | 0.0 | 0.7097 |
| 2.4241 | 7.96 | 1400 | 1.5991 | 32.763 | 25.0389 | 30.7387 | 30.7372 | 15.8514 | 13.9643 | 0.3078 | 0.0 | 0.7127 |
| 2.2984 | 8.96 | 1575 | 1.5373 | 32.7553 | 25.113 | 30.7279 | 30.7385 | 16.1118 | 14.0551 | 0.3085 | 0.0 | 0.7126 |
| 2.2212 | 9.95 | 1750 | 1.4843 | 32.1917 | 24.619 | 30.2246 | 30.2458 | 16.1846 | 14.0741 | 0.3037 | 0.0 | 0.7068 |
| 2.1401 | 10.95 | 1925 | 1.4425 | 32.2614 | 24.7428 | 30.3223 | 30.3377 | 16.3919 | 13.9891 | 0.3044 | 0.0 | 0.7087 |
| 2.0755 | 11.94 | 2100 | 1.4034 | 32.222 | 24.6764 | 30.2975 | 30.3261 | 16.504 | 13.9859 | 0.3043 | 0.0 | 0.71 |
| 2.0328 | 12.94 | 2275 | 1.3723 | 32.1828 | 24.6096 | 30.2115 | 30.2389 | 16.5263 | 13.9632 | 0.3038 | 0.0 | 0.7099 |
| 1.9793 | 13.93 | 2450 | 1.3478 | 32.3184 | 24.6774 | 30.333 | 30.3495 | 16.8168 | 14.2392 | 0.3046 | 0.0 | 0.7097 |
| 1.9541 | 14.93 | 2625 | 1.3288 | 39.7212 | 31.117 | 37.1213 | 37.1596 | 26.1835 | 16.4908 | 0.3582 | 0.0 | 0.7527 |
| 1.9287 | 15.92 | 2800 | 1.3136 | 41.2942 | 32.5064 | 38.5652 | 38.6121 | 28.7564 | 17.0243 | 0.3693 | 0.0 | 0.7619 |
| 1.8985 | 16.92 | 2975 | 1.3059 | 41.3069 | 32.5558 | 38.5643 | 38.607 | 28.7815 | 17.0815 | 0.3697 | 0.0 | 0.7619 |
| 1.8938 | 17.91 | 3150 | 1.2985 | 41.4096 | 32.6579 | 38.6483 | 38.7074 | 28.8733 | 17.0759 | 0.3707 | 0.0 | 0.7628 |
| 1.8795 | 18.91 | 3325 | 1.2941 | 41.3841 | 32.6198 | 38.6245 | 38.6833 | 28.8775 | 17.0839 | 0.3704 | 0.0 | 0.7627 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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