File size: 3,104 Bytes
95f966c 6162b81 95f966c 6162b81 95f966c 6162b81 95f966c |
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: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-8front-1body-8rear
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. -->
# t5-base-TEDxJP-8front-1body-8rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4376
- Wer: 0.1693
- Mer: 0.1635
- Wil: 0.2492
- Wip: 0.7508
- Hits: 55925
- Substitutions: 6304
- Deletions: 2358
- Insertions: 2270
- Cer: 0.1339
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6093 | 1.0 | 1457 | 0.4649 | 0.2573 | 0.2324 | 0.3203 | 0.6797 | 54884 | 6813 | 2890 | 6916 | 0.2380 |
| 0.5049 | 2.0 | 2914 | 0.4186 | 0.1791 | 0.1722 | 0.2595 | 0.7405 | 55594 | 6456 | 2537 | 2575 | 0.1432 |
| 0.4746 | 3.0 | 4371 | 0.4147 | 0.1741 | 0.1681 | 0.2539 | 0.7461 | 55665 | 6315 | 2607 | 2324 | 0.1392 |
| 0.4295 | 4.0 | 5828 | 0.4118 | 0.1723 | 0.1661 | 0.2523 | 0.7477 | 55884 | 6360 | 2343 | 2425 | 0.1342 |
| 0.3881 | 5.0 | 7285 | 0.4123 | 0.1696 | 0.1639 | 0.2496 | 0.7504 | 55896 | 6301 | 2390 | 2265 | 0.1397 |
| 0.3151 | 6.0 | 8742 | 0.4174 | 0.1687 | 0.1631 | 0.2482 | 0.7518 | 55924 | 6249 | 2414 | 2236 | 0.1329 |
| 0.2977 | 7.0 | 10199 | 0.4248 | 0.1674 | 0.1618 | 0.2466 | 0.7534 | 56006 | 6227 | 2354 | 2229 | 0.1321 |
| 0.2737 | 8.0 | 11656 | 0.4293 | 0.1685 | 0.1629 | 0.2485 | 0.7515 | 55898 | 6288 | 2401 | 2192 | 0.1340 |
| 0.2574 | 9.0 | 13113 | 0.4374 | 0.1683 | 0.1627 | 0.2480 | 0.7520 | 55930 | 6268 | 2389 | 2212 | 0.1329 |
| 0.2472 | 10.0 | 14570 | 0.4376 | 0.1693 | 0.1635 | 0.2492 | 0.7508 | 55925 | 6304 | 2358 | 2270 | 0.1339 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|