File size: 2,984 Bytes
90eae41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-large-gramatika161k-b16-e10-lr5
results: []
---
<!-- 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. -->
# mt5-large-gramatika161k-b16-e10-lr5
This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0909
- Rouge1: 72.6295
- Rouge2: 67.8521
- Rougel: 72.5471
- Rougelsum: 72.5591
- Gen Len: 18.3276
## 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: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.9659 | 0.63 | 5000 | 0.1455 | 70.1028 | 63.4969 | 69.9738 | 69.9761 | 18.3378 |
| 0.1735 | 1.27 | 10000 | 0.1195 | 71.1156 | 65.2149 | 70.9932 | 71.0038 | 18.3324 |
| 0.1391 | 1.9 | 15000 | 0.1076 | 71.5692 | 66.0226 | 71.4676 | 71.472 | 18.3281 |
| 0.1149 | 2.54 | 20000 | 0.1035 | 71.8135 | 66.4584 | 71.7212 | 71.7292 | 18.3308 |
| 0.1029 | 3.17 | 25000 | 0.0961 | 72.104 | 66.9459 | 72.0139 | 72.0239 | 18.3282 |
| 0.0898 | 3.81 | 30000 | 0.0944 | 72.231 | 67.1623 | 72.1412 | 72.1542 | 18.3314 |
| 0.0803 | 4.44 | 35000 | 0.0926 | 72.3851 | 67.4624 | 72.3051 | 72.3183 | 18.3286 |
| 0.075 | 5.08 | 40000 | 0.0929 | 72.4219 | 67.5102 | 72.3376 | 72.3479 | 18.3298 |
| 0.0665 | 5.71 | 45000 | 0.0917 | 72.5132 | 67.6501 | 72.4271 | 72.4383 | 18.3264 |
| 0.0624 | 6.35 | 50000 | 0.0911 | 72.5711 | 67.771 | 72.4938 | 72.5041 | 18.3283 |
| 0.0588 | 6.98 | 55000 | 0.0909 | 72.6295 | 67.8521 | 72.5471 | 72.5591 | 18.3276 |
| 0.0534 | 7.62 | 60000 | 0.0920 | 72.6475 | 67.9046 | 72.5743 | 72.5853 | 18.3278 |
| 0.0514 | 8.25 | 65000 | 0.0930 | 72.6373 | 67.894 | 72.5612 | 72.5724 | 18.3277 |
| 0.0492 | 8.88 | 70000 | 0.0930 | 72.6593 | 67.9359 | 72.59 | 72.5971 | 18.3273 |
| 0.047 | 9.52 | 75000 | 0.0932 | 72.6906 | 68.01 | 72.6172 | 72.6269 | 18.3264 |
### Framework versions
- Transformers 4.30.1
- Pytorch 1.11.0a0+b6df043
- Datasets 2.12.0
- Tokenizers 0.13.3
|