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# t5-base-TEDxJP-4front-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: 0.4643
- Wer: 0.1751
- Mer: 0.1690
- Wil: 0.2562
- Wip: 0.7438
- Hits: 55598
- Substitutions: 6434
- Deletions: 2555
- Insertions: 2317
- Cer: 0.1374

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6492        | 1.0   | 1457  | 0.4952          | 0.2272 | 0.2114 | 0.3015 | 0.6985 | 54739 | 6847          | 3001      | 4827       | 0.2013 |
| 0.5556        | 2.0   | 2914  | 0.4456          | 0.1899 | 0.1818 | 0.2686 | 0.7314 | 55189 | 6420          | 2978      | 2864       | 0.1558 |
| 0.4942        | 3.0   | 4371  | 0.4423          | 0.1814 | 0.1743 | 0.2614 | 0.7386 | 55493 | 6437          | 2657      | 2623       | 0.1457 |
| 0.4326        | 4.0   | 5828  | 0.4361          | 0.1749 | 0.1690 | 0.2561 | 0.7439 | 55542 | 6419          | 2626      | 2249       | 0.1362 |
| 0.3867        | 5.0   | 7285  | 0.4395          | 0.1752 | 0.1692 | 0.2559 | 0.7441 | 55542 | 6378          | 2667      | 2270       | 0.1374 |
| 0.3501        | 6.0   | 8742  | 0.4487          | 0.1751 | 0.1691 | 0.2565 | 0.7435 | 55598 | 6448          | 2541      | 2323       | 0.1366 |
| 0.3835        | 7.0   | 10199 | 0.4494          | 0.1744 | 0.1685 | 0.2556 | 0.7444 | 55594 | 6416          | 2577      | 2274       | 0.1378 |
| 0.3013        | 8.0   | 11656 | 0.4580          | 0.1744 | 0.1685 | 0.2563 | 0.7437 | 55570 | 6467          | 2550      | 2248       | 0.1366 |
| 0.3126        | 9.0   | 13113 | 0.4598          | 0.1749 | 0.1689 | 0.2564 | 0.7436 | 55571 | 6447          | 2569      | 2281       | 0.1376 |
| 0.3089        | 10.0  | 14570 | 0.4643          | 0.1751 | 0.1690 | 0.2562 | 0.7438 | 55598 | 6434          | 2555      | 2317       | 0.1374 |


### 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-4front-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-4front-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: 0.4643
* Wer: 0.1751
* Mer: 0.1690
* Wil: 0.2562
* Wip: 0.7438
* Hits: 55598
* Substitutions: 6434
* Deletions: 2555
* Insertions: 2317
* Cer: 0.1374


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