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# mt5-base-finetuned-novel-chinese-to-spanish-v1

This model is a fine-tuned version of [quickman/mt5-base-finetuned-chinese-to-spanish](https://huggingface.co/quickman/mt5-base-finetuned-chinese-to-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2288
- Score: 0.0063
- Counts: [609, 331, 205, 120]
- Totals: [838, 774, 710, 646]
- Precisions: [72.67303102625299, 42.76485788113695, 28.87323943661972, 18.575851393188856]
- Bp: 0.0002
- Sys Len: 838
- Ref Len: 8089
- Bleu: 0.0063
- 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: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Score  | Counts               | Totals               | Precisions                                                                      | Bp     | Sys Len | Ref Len | Bleu   | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------------------:|:--------------------:|:-------------------------------------------------------------------------------:|:------:|:-------:|:-------:|:------:|:-------:|
| 2.7093        | 0.28  | 500   | 1.9080          | 0.0035 | [510, 185, 91, 37]   | [848, 784, 720, 656] | [60.14150943396226, 23.596938775510203, 12.63888888888889, 5.640243902439025]   | 0.0002 | 848     | 8089    | 0.0035 | 19.0    |
| 2.4994        | 0.55  | 1000  | 1.7520          | 0.0036 | [524, 199, 100, 46]  | [842, 778, 714, 650] | [62.23277909738717, 25.57840616966581, 14.005602240896359, 7.076923076923077]   | 0.0002 | 842     | 8089    | 0.0036 | 19.0    |
| 2.3427        | 0.83  | 1500  | 1.6632          | 0.0040 | [530, 212, 109, 53]  | [844, 780, 716, 652] | [62.796208530805686, 27.17948717948718, 15.223463687150838, 8.128834355828221]  | 0.0002 | 844     | 8089    | 0.0040 | 19.0    |
| 2.211         | 1.1   | 2000  | 1.5980          | 0.0050 | [548, 230, 123, 66]  | [855, 791, 727, 663] | [64.09356725146199, 29.077117572692792, 16.91884456671252, 9.95475113122172]    | 0.0002 | 855     | 8089    | 0.0050 | 19.0    |
| 2.1536        | 1.38  | 2500  | 1.5442          | 0.0053 | [552, 239, 137, 77]  | [852, 788, 724, 660] | [64.78873239436619, 30.32994923857868, 18.92265193370166, 11.666666666666666]   | 0.0002 | 852     | 8089    | 0.0053 | 19.0    |
| 2.079         | 1.66  | 3000  | 1.5088          | 0.0055 | [551, 244, 142, 84]  | [854, 790, 726, 662] | [64.51990632318501, 30.88607594936709, 19.55922865013774, 12.688821752265861]   | 0.0002 | 854     | 8089    | 0.0055 | 19.0    |
| 2.0374        | 1.93  | 3500  | 1.4768          | 0.0054 | [557, 259, 149, 83]  | [849, 785, 721, 657] | [65.60659599528857, 32.99363057324841, 20.665742024965326, 12.633181126331811]  | 0.0002 | 849     | 8089    | 0.0054 | 19.0    |
| 2.0064        | 2.21  | 4000  | 1.4418          | 0.0054 | [559, 266, 157, 91]  | [844, 780, 716, 652] | [66.23222748815166, 34.1025641025641, 21.92737430167598, 13.957055214723926]    | 0.0002 | 844     | 8089    | 0.0054 | 19.0    |
| 1.9536        | 2.48  | 4500  | 1.4194          | 0.0056 | [557, 260, 157, 87]  | [849, 785, 721, 657] | [65.60659599528857, 33.12101910828026, 21.7753120665742, 13.242009132420092]    | 0.0002 | 849     | 8089    | 0.0056 | 19.0    |
| 1.9436        | 2.76  | 5000  | 1.4030          | 0.0051 | [561, 262, 151, 85]  | [841, 777, 713, 649] | [66.70630202140309, 33.71943371943372, 21.1781206171108, 13.097072419106317]    | 0.0002 | 841     | 8089    | 0.0051 | 19.0    |
| 1.8939        | 3.04  | 5500  | 1.3826          | 0.0059 | [568, 277, 169, 99]  | [848, 784, 720, 656] | [66.98113207547169, 35.33163265306123, 23.47222222222222, 15.091463414634147]   | 0.0002 | 848     | 8089    | 0.0059 | 19.0    |
| 1.8497        | 3.31  | 6000  | 1.3649          | 0.0059 | [576, 288, 180, 107] | [843, 779, 715, 651] | [68.32740213523131, 36.97047496790757, 25.174825174825173, 16.43625192012289]   | 0.0002 | 843     | 8089    | 0.0059 | 19.0    |
| 1.8177        | 3.59  | 6500  | 1.3575          | 0.0060 | [585, 285, 173, 98]  | [847, 783, 719, 655] | [69.06729634002362, 36.39846743295019, 24.061196105702365, 14.961832061068701]  | 0.0002 | 847     | 8089    | 0.0060 | 19.0    |
| 1.8368        | 3.86  | 7000  | 1.3428          | 0.0061 | [583, 285, 171, 95]  | [851, 787, 723, 659] | [68.50763807285547, 36.213468869123254, 23.651452282157678, 14.41578148710167]  | 0.0002 | 851     | 8089    | 0.0061 | 19.0    |
| 1.7906        | 4.14  | 7500  | 1.3295          | 0.0059 | [581, 284, 167, 88]  | [850, 786, 722, 658] | [68.3529411764706, 36.1323155216285, 23.130193905817176, 13.373860182370821]    | 0.0002 | 850     | 8089    | 0.0059 | 19.0    |
| 1.766         | 4.42  | 8000  | 1.3204          | 0.0057 | [575, 279, 161, 89]  | [848, 784, 720, 656] | [67.80660377358491, 35.58673469387755, 22.36111111111111, 13.567073170731707]   | 0.0002 | 848     | 8089    | 0.0057 | 19.0    |
| 1.7615        | 4.69  | 8500  | 1.3124          | 0.0061 | [590, 293, 176, 100] | [848, 784, 720, 656] | [69.5754716981132, 37.37244897959184, 24.444444444444443, 15.24390243902439]    | 0.0002 | 848     | 8089    | 0.0061 | 19.0    |
| 1.7741        | 4.97  | 9000  | 1.3057          | 0.0062 | [590, 298, 180, 105] | [846, 782, 718, 654] | [69.73995271867612, 38.107416879795394, 25.069637883008358, 16.05504587155963]  | 0.0002 | 846     | 8089    | 0.0062 | 19.0    |
| 1.7266        | 5.24  | 9500  | 1.2969          | 0.0062 | [592, 304, 182, 104] | [846, 782, 718, 654] | [69.97635933806147, 38.87468030690537, 25.348189415041784, 15.902140672782874]  | 0.0002 | 846     | 8089    | 0.0062 | 19.0    |
| 1.7309        | 5.52  | 10000 | 1.2904          | 0.0054 | [580, 287, 166, 88]  | [840, 776, 712, 648] | [69.04761904761905, 36.98453608247423, 23.314606741573034, 13.580246913580247]  | 0.0002 | 840     | 8089    | 0.0054 | 19.0    |
| 1.6973        | 5.79  | 10500 | 1.2818          | 0.0059 | [591, 302, 179, 100] | [842, 778, 714, 650] | [70.19002375296913, 38.81748071979435, 25.07002801120448, 15.384615384615385]   | 0.0002 | 842     | 8089    | 0.0059 | 19.0    |
| 1.6613        | 6.07  | 11000 | 1.2757          | 0.0058 | [596, 302, 185, 102] | [840, 776, 712, 648] | [70.95238095238095, 38.91752577319588, 25.98314606741573, 15.74074074074074]    | 0.0002 | 840     | 8089    | 0.0058 | 19.0    |
| 1.6699        | 6.35  | 11500 | 1.2689          | 0.0063 | [600, 316, 197, 113] | [842, 778, 714, 650] | [71.25890736342043, 40.616966580976865, 27.591036414565828, 17.384615384615383] | 0.0002 | 842     | 8089    | 0.0063 | 19.0    |
| 1.6566        | 6.62  | 12000 | 1.2630          | 0.0064 | [610, 320, 194, 109] | [844, 780, 716, 652] | [72.27488151658768, 41.02564102564103, 27.094972067039105, 16.717791411042946]  | 0.0002 | 844     | 8089    | 0.0064 | 19.0    |
| 1.6417        | 6.9   | 12500 | 1.2592          | 0.0065 | [606, 325, 201, 116] | [843, 779, 715, 651] | [71.88612099644128, 41.7201540436457, 28.111888111888113, 17.81874039938556]    | 0.0002 | 843     | 8089    | 0.0065 | 19.0    |
| 1.6703        | 7.17  | 13000 | 1.2531          | 0.0072 | [616, 325, 198, 113] | [855, 791, 727, 663] | [72.046783625731, 41.08723135271808, 27.235213204951858, 17.043740573152338]    | 0.0002 | 855     | 8089    | 0.0072 | 19.0    |
| 1.6283        | 7.45  | 13500 | 1.2508          | 0.0069 | [614, 334, 209, 122] | [846, 782, 718, 654] | [72.57683215130024, 42.710997442455245, 29.108635097493035, 18.654434250764528] | 0.0002 | 846     | 8089    | 0.0069 | 19.0    |
| 1.6139        | 7.73  | 14000 | 1.2485          | 0.0056 | [595, 315, 192, 111] | [833, 769, 705, 641] | [71.42857142857143, 40.96228868660598, 27.23404255319149, 17.316692667706707]   | 0.0002 | 833     | 8089    | 0.0056 | 19.0    |
| 1.6203        | 8.0   | 14500 | 1.2425          | 0.0067 | [613, 329, 203, 119] | [845, 781, 717, 653] | [72.54437869822485, 42.12548015364917, 28.312412831241282, 18.223583460949463]  | 0.0002 | 845     | 8089    | 0.0067 | 19.0    |
| 1.6289        | 8.28  | 15000 | 1.2414          | 0.0061 | [603, 322, 200, 119] | [837, 773, 709, 645] | [72.04301075268818, 41.65588615782665, 28.208744710860366, 18.449612403100776]  | 0.0002 | 837     | 8089    | 0.0061 | 19.0    |
| 1.6301        | 8.55  | 15500 | 1.2386          | 0.0063 | [610, 328, 205, 123] | [838, 774, 710, 646] | [72.79236276849642, 42.377260981912144, 28.87323943661972, 19.040247678018577]  | 0.0002 | 838     | 8089    | 0.0063 | 19.0    |
| 1.5992        | 8.83  | 16000 | 1.2379          | 0.0061 | [603, 323, 200, 119] | [837, 773, 709, 645] | [72.04301075268818, 41.785252263906855, 28.208744710860366, 18.449612403100776] | 0.0002 | 837     | 8089    | 0.0061 | 19.0    |
| 1.5984        | 9.11  | 16500 | 1.2367          | 0.0060 | [597, 317, 195, 116] | [837, 773, 709, 645] | [71.32616487455198, 41.00905562742562, 27.50352609308886, 17.984496124031008]   | 0.0002 | 837     | 8089    | 0.0060 | 19.0    |
| 1.6026        | 9.38  | 17000 | 1.2336          | 0.0063 | [606, 326, 204, 124] | [838, 774, 710, 646] | [72.31503579952268, 42.11886304909561, 28.732394366197184, 19.195046439628484]  | 0.0002 | 838     | 8089    | 0.0063 | 19.0    |
| 1.6059        | 9.66  | 17500 | 1.2319          | 0.0061 | [606, 330, 206, 123] | [835, 771, 707, 643] | [72.57485029940119, 42.80155642023346, 29.13719943422914, 19.12908242612753]    | 0.0002 | 835     | 8089    | 0.0061 | 19.0    |
| 1.6227        | 9.93  | 18000 | 1.2294          | 0.0063 | [609, 334, 209, 122] | [837, 773, 709, 645] | [72.75985663082437, 43.20827943078913, 29.478138222849083, 18.914728682170544]  | 0.0002 | 837     | 8089    | 0.0063 | 19.0    |
| 1.6031        | 10.21 | 18500 | 1.2300          | 0.0060 | [605, 328, 203, 120] | [835, 771, 707, 643] | [72.45508982035928, 42.54215304798962, 28.712871287128714, 18.662519440124417]  | 0.0002 | 835     | 8089    | 0.0060 | 19.0    |
| 1.5746        | 10.49 | 19000 | 1.2301          | 0.0064 | [612, 335, 209, 123] | [838, 774, 710, 646] | [73.0310262529833, 43.281653746770026, 29.43661971830986, 19.040247678018577]   | 0.0002 | 838     | 8089    | 0.0064 | 19.0    |
| 1.5689        | 10.76 | 19500 | 1.2288          | 0.0063 | [609, 331, 205, 120] | [838, 774, 710, 646] | [72.67303102625299, 42.76485788113695, 28.87323943661972, 18.575851393188856]   | 0.0002 | 838     | 8089    | 0.0063 | 19.0    |
| 1.5928        | 11.04 | 20000 | 1.2288          | 0.0063 | [609, 331, 205, 120] | [838, 774, 710, 646] | [72.67303102625299, 42.76485788113695, 28.87323943661972, 18.575851393188856]   | 0.0002 | 838     | 8089    | 0.0063 | 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-v1", "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-v1
==============================================


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


* Loss: 1.2288
* Score: 0.0063
* Counts: [609, 331, 205, 120]
* Totals: [838, 774, 710, 646]
* Precisions: [72.67303102625299, 42.76485788113695, 28.87323943661972, 18.575851393188856]
* Bp: 0.0002
* Sys Len: 838
* Ref Len: 8089
* Bleu: 0.0063
* 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: 20000


### 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: 20000 ### 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: 20000 ### 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: 20000### Training results### Framework versions


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