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# legalectra-small-spanish-becasv3-6

This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8441

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 150

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 5    | 5.6469          |
| No log        | 2.0   | 10   | 5.5104          |
| No log        | 3.0   | 15   | 5.4071          |
| No log        | 4.0   | 20   | 5.3313          |
| No log        | 5.0   | 25   | 5.2629          |
| No log        | 6.0   | 30   | 5.1972          |
| No log        | 7.0   | 35   | 5.1336          |
| No log        | 8.0   | 40   | 5.0667          |
| No log        | 9.0   | 45   | 5.0030          |
| No log        | 10.0  | 50   | 4.9302          |
| No log        | 11.0  | 55   | 4.8646          |
| No log        | 12.0  | 60   | 4.7963          |
| No log        | 13.0  | 65   | 4.7328          |
| No log        | 14.0  | 70   | 4.6735          |
| No log        | 15.0  | 75   | 4.6258          |
| No log        | 16.0  | 80   | 4.5869          |
| No log        | 17.0  | 85   | 4.5528          |
| No log        | 18.0  | 90   | 4.5177          |
| No log        | 19.0  | 95   | 4.4916          |
| No log        | 20.0  | 100  | 4.4685          |
| No log        | 21.0  | 105  | 4.4371          |
| No log        | 22.0  | 110  | 4.4271          |
| No log        | 23.0  | 115  | 4.3905          |
| No log        | 24.0  | 120  | 4.3931          |
| No log        | 25.0  | 125  | 4.3902          |
| No log        | 26.0  | 130  | 4.3772          |
| No log        | 27.0  | 135  | 4.3981          |
| No log        | 28.0  | 140  | 4.4463          |
| No log        | 29.0  | 145  | 4.4501          |
| No log        | 30.0  | 150  | 4.4654          |
| No log        | 31.0  | 155  | 4.4069          |
| No log        | 32.0  | 160  | 4.4108          |
| No log        | 33.0  | 165  | 4.4394          |
| No log        | 34.0  | 170  | 4.4320          |
| No log        | 35.0  | 175  | 4.3541          |
| No log        | 36.0  | 180  | 4.4534          |
| No log        | 37.0  | 185  | 4.2616          |
| No log        | 38.0  | 190  | 4.2474          |
| No log        | 39.0  | 195  | 4.4358          |
| No log        | 40.0  | 200  | 4.3060          |
| No log        | 41.0  | 205  | 4.1866          |
| No log        | 42.0  | 210  | 4.2735          |
| No log        | 43.0  | 215  | 4.2739          |
| No log        | 44.0  | 220  | 4.1812          |
| No log        | 45.0  | 225  | 4.2484          |
| No log        | 46.0  | 230  | 4.3706          |
| No log        | 47.0  | 235  | 4.3487          |
| No log        | 48.0  | 240  | 4.2805          |
| No log        | 49.0  | 245  | 4.3180          |
| No log        | 50.0  | 250  | 4.3574          |
| No log        | 51.0  | 255  | 4.2823          |
| No log        | 52.0  | 260  | 4.0643          |
| No log        | 53.0  | 265  | 4.0729          |
| No log        | 54.0  | 270  | 4.2368          |
| No log        | 55.0  | 275  | 4.2845          |
| No log        | 56.0  | 280  | 4.1009          |
| No log        | 57.0  | 285  | 4.0629          |
| No log        | 58.0  | 290  | 4.1250          |
| No log        | 59.0  | 295  | 4.2048          |
| No log        | 60.0  | 300  | 4.2412          |
| No log        | 61.0  | 305  | 4.1653          |
| No log        | 62.0  | 310  | 4.1433          |
| No log        | 63.0  | 315  | 4.1309          |
| No log        | 64.0  | 320  | 4.1381          |
| No log        | 65.0  | 325  | 4.2162          |
| No log        | 66.0  | 330  | 4.1858          |
| No log        | 67.0  | 335  | 4.1342          |
| No log        | 68.0  | 340  | 4.1247          |
| No log        | 69.0  | 345  | 4.1701          |
| No log        | 70.0  | 350  | 4.1915          |
| No log        | 71.0  | 355  | 4.1356          |
| No log        | 72.0  | 360  | 4.1766          |
| No log        | 73.0  | 365  | 4.1296          |
| No log        | 74.0  | 370  | 4.0594          |
| No log        | 75.0  | 375  | 4.0601          |
| No log        | 76.0  | 380  | 4.0328          |
| No log        | 77.0  | 385  | 3.9978          |
| No log        | 78.0  | 390  | 4.0070          |
| No log        | 79.0  | 395  | 4.0519          |
| No log        | 80.0  | 400  | 4.1000          |
| No log        | 81.0  | 405  | 3.9550          |
| No log        | 82.0  | 410  | 3.9159          |
| No log        | 83.0  | 415  | 3.9494          |
| No log        | 84.0  | 420  | 4.0546          |
| No log        | 85.0  | 425  | 4.2223          |
| No log        | 86.0  | 430  | 4.2665          |
| No log        | 87.0  | 435  | 3.8892          |
| No log        | 88.0  | 440  | 3.7763          |
| No log        | 89.0  | 445  | 3.8576          |
| No log        | 90.0  | 450  | 4.0089          |
| No log        | 91.0  | 455  | 4.1495          |
| No log        | 92.0  | 460  | 4.1545          |
| No log        | 93.0  | 465  | 4.0164          |
| No log        | 94.0  | 470  | 3.9175          |
| No log        | 95.0  | 475  | 3.9308          |
| No log        | 96.0  | 480  | 3.9658          |
| No log        | 97.0  | 485  | 3.9856          |
| No log        | 98.0  | 490  | 3.9691          |
| No log        | 99.0  | 495  | 3.9082          |
| 3.2873        | 100.0 | 500  | 3.8736          |
| 3.2873        | 101.0 | 505  | 3.8963          |
| 3.2873        | 102.0 | 510  | 3.9391          |
| 3.2873        | 103.0 | 515  | 3.9408          |
| 3.2873        | 104.0 | 520  | 3.9075          |
| 3.2873        | 105.0 | 525  | 3.8258          |
| 3.2873        | 106.0 | 530  | 3.7917          |
| 3.2873        | 107.0 | 535  | 3.7981          |
| 3.2873        | 108.0 | 540  | 3.8272          |
| 3.2873        | 109.0 | 545  | 3.8655          |
| 3.2873        | 110.0 | 550  | 3.8234          |
| 3.2873        | 111.0 | 555  | 3.7126          |
| 3.2873        | 112.0 | 560  | 3.6981          |
| 3.2873        | 113.0 | 565  | 3.7327          |
| 3.2873        | 114.0 | 570  | 3.8470          |
| 3.2873        | 115.0 | 575  | 4.0036          |
| 3.2873        | 116.0 | 580  | 4.0412          |
| 3.2873        | 117.0 | 585  | 4.0487          |
| 3.2873        | 118.0 | 590  | 4.0524          |
| 3.2873        | 119.0 | 595  | 4.0375          |
| 3.2873        | 120.0 | 600  | 3.9971          |
| 3.2873        | 121.0 | 605  | 3.8959          |
| 3.2873        | 122.0 | 610  | 3.8834          |
| 3.2873        | 123.0 | 615  | 3.9279          |
| 3.2873        | 124.0 | 620  | 3.9374          |
| 3.2873        | 125.0 | 625  | 3.9515          |
| 3.2873        | 126.0 | 630  | 3.9625          |
| 3.2873        | 127.0 | 635  | 3.9635          |
| 3.2873        | 128.0 | 640  | 3.9596          |
| 3.2873        | 129.0 | 645  | 3.8871          |
| 3.2873        | 130.0 | 650  | 3.8307          |
| 3.2873        | 131.0 | 655  | 3.8318          |
| 3.2873        | 132.0 | 660  | 3.8403          |
| 3.2873        | 133.0 | 665  | 3.8560          |
| 3.2873        | 134.0 | 670  | 3.8650          |
| 3.2873        | 135.0 | 675  | 3.8734          |
| 3.2873        | 136.0 | 680  | 3.8756          |
| 3.2873        | 137.0 | 685  | 3.8613          |
| 3.2873        | 138.0 | 690  | 3.8447          |
| 3.2873        | 139.0 | 695  | 3.8362          |
| 3.2873        | 140.0 | 700  | 3.8328          |
| 3.2873        | 141.0 | 705  | 3.8350          |
| 3.2873        | 142.0 | 710  | 3.8377          |
| 3.2873        | 143.0 | 715  | 3.8399          |
| 3.2873        | 144.0 | 720  | 3.8414          |
| 3.2873        | 145.0 | 725  | 3.8422          |
| 3.2873        | 146.0 | 730  | 3.8435          |
| 3.2873        | 147.0 | 735  | 3.8437          |
| 3.2873        | 148.0 | 740  | 3.8437          |
| 3.2873        | 149.0 | 745  | 3.8440          |
| 3.2873        | 150.0 | 750  | 3.8441          |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "datasets": ["becasv2"], "model-index": [{"name": "legalectra-small-spanish-becasv3-6", "results": []}]}
transformers pytorch tensorboard electra question-answering generated_from_trainer dataset:becasv2 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us 

legalectra-small-spanish-becasv3-6
==================================


This model is a fine-tuned version of mrm8488/legalectra-small-spanish on the becasv2 dataset.
It achieves the following results on the evaluation set:


* Loss: 3.8441


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-becasv2 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 150### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1