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# t5-base-TEDxJP-8front-1body-0rear

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: 0.4589
- Wer: 0.1739
- Mer: 0.1679
- Wil: 0.2545
- Wip: 0.7455
- Hits: 55667
- Substitutions: 6385
- Deletions: 2535
- Insertions: 2309
- Cer: 0.1363

## 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: 42
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6586        | 1.0   | 1457  | 0.4812          | 0.2110 | 0.1994 | 0.2888 | 0.7112 | 54745 | 6712          | 3130      | 3789       | 0.1784 |
| 0.5246        | 2.0   | 2914  | 0.4383          | 0.1839 | 0.1770 | 0.2641 | 0.7359 | 55251 | 6428          | 2908      | 2544       | 0.1481 |
| 0.4795        | 3.0   | 4371  | 0.4327          | 0.1811 | 0.1740 | 0.2610 | 0.7390 | 55523 | 6438          | 2626      | 2631       | 0.1458 |
| 0.4224        | 4.0   | 5828  | 0.4328          | 0.1754 | 0.1693 | 0.2555 | 0.7445 | 55577 | 6338          | 2672      | 2318       | 0.1397 |
| 0.3755        | 5.0   | 7285  | 0.4351          | 0.1723 | 0.1668 | 0.2529 | 0.7471 | 55607 | 6326          | 2654      | 2150       | 0.1362 |
| 0.3538        | 6.0   | 8742  | 0.4413          | 0.1728 | 0.1670 | 0.2531 | 0.7469 | 55696 | 6341          | 2550      | 2271       | 0.1372 |
| 0.3686        | 7.0   | 10199 | 0.4455          | 0.1715 | 0.1659 | 0.2519 | 0.7481 | 55692 | 6319          | 2576      | 2180       | 0.1354 |
| 0.3004        | 8.0   | 11656 | 0.4518          | 0.1727 | 0.1668 | 0.2537 | 0.7463 | 55712 | 6400          | 2475      | 2281       | 0.1371 |
| 0.2914        | 9.0   | 13113 | 0.4564          | 0.1739 | 0.1678 | 0.2544 | 0.7456 | 55681 | 6378          | 2528      | 2323       | 0.1370 |
| 0.297         | 10.0  | 14570 | 0.4589          | 0.1739 | 0.1679 | 0.2545 | 0.7455 | 55667 | 6385          | 2535      | 2309       | 0.1363 |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["te_dx_jp"], "model-index": [{"name": "t5-base-TEDxJP-8front-1body-0rear", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:te_dx_jp license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-base-TEDxJP-8front-1body-0rear
=================================


This model is a fine-tuned version of sonoisa/t5-base-japanese on the te\_dx\_jp dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4589
* Wer: 0.1739
* Mer: 0.1679
* Wil: 0.2545
* Wip: 0.7455
* Hits: 55667
* Substitutions: 6385
* Deletions: 2535
* Insertions: 2309
* Cer: 0.1363


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: 42
* 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



### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-te_dx_jp #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu116
* Datasets 2.4.0
* Tokenizers 0.12.1