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# mt5-base-finetuned-novel-chinese-to-spanish

This model is a fine-tuned version of [quickman/mt5-base-finetuned-chinese-to-spanish-finetuned-chinese-to-spanish](https://huggingface.co/quickman/mt5-base-finetuned-chinese-to-spanish-finetuned-chinese-to-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3193
- Score: 0.0000
- Counts: [545, 246, 135, 80]
- Totals: [777, 713, 649, 585]
- Precisions: [70.14157014157014, 34.50210378681627, 20.801232665639446, 13.675213675213675]
- Bp: 0.0000
- Sys Len: 777
- Ref Len: 17012
- Bleu: 0.0000
- Gen Len: 19.0

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Score  | Counts              | Totals               | Precisions                                                                     | Bp     | Sys Len | Ref Len | Bleu   | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------------------:|:--------------------:|:------------------------------------------------------------------------------:|:------:|:-------:|:-------:|:------:|:-------:|
| 2.7861        | 0.6   | 500   | 1.9548          | 0.0000 | [465, 147, 51, 23]  | [754, 690, 626, 562] | [61.6710875331565, 21.304347826086957, 8.146964856230031, 4.092526690391459]   | 0.0000 | 754     | 17012   | 0.0000 | 19.0    |
| 2.5103        | 1.19  | 1000  | 1.7626          | 0.0000 | [491, 174, 62, 24]  | [770, 706, 642, 578] | [63.76623376623377, 24.64589235127479, 9.657320872274143, 4.1522491349480966]  | 0.0000 | 770     | 17012   | 0.0000 | 19.0    |
| 2.3148        | 1.79  | 1500  | 1.6428          | 0.0000 | [499, 181, 73, 35]  | [781, 717, 653, 589] | [63.892445582586426, 25.24407252440725, 11.179173047473201, 5.942275042444821] | 0.0000 | 781     | 17012   | 0.0000 | 19.0    |
| 2.17          | 2.39  | 2000  | 1.5580          | 0.0000 | [524, 201, 90, 44]  | [784, 720, 656, 592] | [66.83673469387755, 27.916666666666668, 13.71951219512195, 7.4324324324324325] | 0.0000 | 784     | 17012   | 0.0000 | 19.0    |
| 2.0889        | 2.99  | 2500  | 1.5197          | 0.0000 | [529, 214, 102, 55] | [781, 717, 653, 589] | [67.73367477592829, 29.846582984658298, 15.620214395099541, 9.33786078098472]  | 0.0000 | 781     | 17012   | 0.0000 | 19.0    |
| 2.009         | 3.58  | 3000  | 1.4945          | 0.0000 | [527, 217, 103, 59] | [789, 725, 661, 597] | [66.7934093789607, 29.93103448275862, 15.582450832072617, 9.882747068676716]   | 0.0000 | 789     | 17012   | 0.0000 | 19.0    |
| 1.9494        | 4.18  | 3500  | 1.4647          | 0.0000 | [518, 214, 105, 60] | [774, 710, 646, 582] | [66.9250645994832, 30.140845070422536, 16.25386996904025, 10.309278350515465]  | 0.0000 | 774     | 17012   | 0.0000 | 19.0    |
| 1.9289        | 4.78  | 4000  | 1.4282          | 0.0000 | [539, 234, 116, 66] | [781, 717, 653, 589] | [69.01408450704226, 32.63598326359833, 17.76416539050536, 11.205432937181664]  | 0.0000 | 781     | 17012   | 0.0000 | 19.0    |
| 1.8661        | 5.38  | 4500  | 1.4049          | 0.0000 | [520, 217, 117, 74] | [763, 699, 635, 571] | [68.15203145478375, 31.044349070100143, 18.4251968503937, 12.959719789842381]  | 0.0000 | 763     | 17012   | 0.0000 | 19.0    |
| 1.8417        | 5.97  | 5000  | 1.3815          | 0.0000 | [536, 235, 119, 71] | [774, 710, 646, 582] | [69.25064599483204, 33.098591549295776, 18.42105263157895, 12.199312714776632] | 0.0000 | 774     | 17012   | 0.0000 | 19.0    |
| 1.8094        | 6.57  | 5500  | 1.3651          | 0.0000 | [528, 226, 117, 68] | [765, 701, 637, 573] | [69.01960784313725, 32.23965763195435, 18.367346938775512, 11.8673647469459]   | 0.0000 | 765     | 17012   | 0.0000 | 19.0    |
| 1.811         | 7.17  | 6000  | 1.3629          | 0.0000 | [526, 225, 119, 69] | [768, 704, 640, 576] | [68.48958333333333, 31.960227272727273, 18.59375, 11.979166666666666]          | 0.0000 | 768     | 17012   | 0.0000 | 19.0    |
| 1.7635        | 7.77  | 6500  | 1.3451          | 0.0000 | [529, 230, 124, 72] | [765, 701, 637, 573] | [69.15032679738562, 32.810271041369475, 19.46624803767661, 12.565445026178011] | 0.0000 | 765     | 17012   | 0.0000 | 19.0    |
| 1.7782        | 8.36  | 7000  | 1.3376          | 0.0000 | [530, 240, 132, 79] | [771, 707, 643, 579] | [68.74189364461738, 33.946251768033946, 20.52877138413686, 13.644214162348877] | 0.0000 | 771     | 17012   | 0.0000 | 19.0    |
| 1.7528        | 8.96  | 7500  | 1.3305          | 0.0000 | [543, 242, 129, 78] | [779, 715, 651, 587] | [69.70474967907573, 33.84615384615385, 19.81566820276498, 13.287904599659285]  | 0.0000 | 779     | 17012   | 0.0000 | 19.0    |
| 1.7365        | 9.56  | 8000  | 1.3273          | 0.0000 | [532, 232, 123, 73] | [770, 706, 642, 578] | [69.0909090909091, 32.861189801699716, 19.1588785046729, 12.629757785467127]   | 0.0000 | 770     | 17012   | 0.0000 | 19.0    |
| 1.7212        | 10.16 | 8500  | 1.3247          | 0.0000 | [544, 245, 136, 80] | [777, 713, 649, 585] | [70.01287001287001, 34.36185133239832, 20.955315870570107, 13.675213675213675] | 0.0000 | 777     | 17012   | 0.0000 | 19.0    |
| 1.7027        | 10.75 | 9000  | 1.3229          | 0.0000 | [548, 244, 131, 77] | [776, 712, 648, 584] | [70.61855670103093, 34.26966292134831, 20.21604938271605, 13.184931506849315]  | 0.0000 | 776     | 17012   | 0.0000 | 19.0    |
| 1.702         | 11.35 | 9500  | 1.3198          | 0.0000 | [544, 247, 137, 82] | [774, 710, 646, 582] | [70.2842377260982, 34.7887323943662, 21.207430340557277, 14.0893470790378]     | 0.0000 | 774     | 17012   | 0.0000 | 19.0    |
| 1.7258        | 11.95 | 10000 | 1.3193          | 0.0000 | [545, 246, 135, 80] | [777, 713, 649, 585] | [70.14157014157014, 34.50210378681627, 20.801232665639446, 13.675213675213675] | 0.0000 | 777     | 17012   | 0.0000 | 19.0    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mt5-base-finetuned-novel-chinese-to-spanish", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

mt5-base-finetuned-novel-chinese-to-spanish
===========================================


This model is a fine-tuned version of quickman/mt5-base-finetuned-chinese-to-spanish-finetuned-chinese-to-spanish on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.3193
* Score: 0.0000
* Counts: [545, 246, 135, 80]
* Totals: [777, 713, 649, 585]
* Precisions: [70.14157014157014, 34.50210378681627, 20.801232665639446, 13.675213675213675]
* Bp: 0.0000
* Sys Len: 777
* Ref Len: 17012
* Bleu: 0.0000
* Gen Len: 19.0


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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 40
* training\_steps: 10000


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 40
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 40
* training\_steps: 10000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 40
* training\_steps: 10000### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3