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# t5-base-TEDxJP-0front-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: 0.5110
- Wer: 0.1852
- Mer: 0.1786
- Wil: 0.2694
- Wip: 0.7306
- Hits: 55023
- Substitutions: 6739
- Deletions: 2825
- Insertions: 2397
- Cer: 0.1459

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6898        | 1.0   | 1457  | 0.5259          | 0.2378 | 0.2201 | 0.3112 | 0.6888 | 54412 | 6955          | 3220      | 5183       | 0.2118 |
| 0.5915        | 2.0   | 2914  | 0.4905          | 0.1893 | 0.1824 | 0.2734 | 0.7266 | 54815 | 6756          | 3016      | 2455       | 0.1588 |
| 0.5414        | 3.0   | 4371  | 0.4812          | 0.1933 | 0.1850 | 0.2748 | 0.7252 | 54989 | 6684          | 2914      | 2885       | 0.1605 |
| 0.4633        | 4.0   | 5828  | 0.4820          | 0.1847 | 0.1782 | 0.2685 | 0.7315 | 54999 | 6685          | 2903      | 2342       | 0.1451 |
| 0.4275        | 5.0   | 7285  | 0.4831          | 0.1851 | 0.1785 | 0.2681 | 0.7319 | 55034 | 6630          | 2923      | 2405       | 0.1491 |
| 0.3977        | 6.0   | 8742  | 0.4903          | 0.1836 | 0.1773 | 0.2676 | 0.7324 | 54996 | 6681          | 2910      | 2264       | 0.1451 |
| 0.4236        | 7.0   | 10199 | 0.4941          | 0.1853 | 0.1788 | 0.2693 | 0.7307 | 54964 | 6706          | 2917      | 2343       | 0.1451 |
| 0.3496        | 8.0   | 11656 | 0.5022          | 0.1861 | 0.1794 | 0.2693 | 0.7307 | 54979 | 6661          | 2947      | 2409       | 0.1516 |
| 0.3439        | 9.0   | 13113 | 0.5081          | 0.1872 | 0.1802 | 0.2709 | 0.7291 | 55016 | 6738          | 2833      | 2519       | 0.1606 |
| 0.3505        | 10.0  | 14570 | 0.5110          | 0.1852 | 0.1786 | 0.2694 | 0.7306 | 55023 | 6739          | 2825      | 2397       | 0.1459 |


### 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-0front-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-0front-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: 0.5110
* Wer: 0.1852
* Mer: 0.1786
* Wil: 0.2694
* Wip: 0.7306
* Hits: 55023
* Substitutions: 6739
* Deletions: 2825
* Insertions: 2397
* Cer: 0.1459


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