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# longformer-base-4096-bne-es-finetuned

This model is a fine-tuned version of [joheras/longformer-base-4096-bne-es-finetuned-clinais](https://huggingface.co/joheras/longformer-base-4096-bne-es-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4271
- Precision: 0.5208
- Recall: 0.6368
- F1: 0.5730
- Accuracy: 0.8522

## 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
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 196   | 0.6543          | 0.2024    | 0.3679 | 0.2611 | 0.8004   |
| No log        | 2.0   | 392   | 0.5462          | 0.2347    | 0.4047 | 0.2971 | 0.8296   |
| 0.7514        | 3.0   | 588   | 0.5546          | 0.2902    | 0.5075 | 0.3693 | 0.8378   |
| 0.7514        | 4.0   | 784   | 0.5725          | 0.2885    | 0.5019 | 0.3664 | 0.8333   |
| 0.7514        | 5.0   | 980   | 0.5880          | 0.3329    | 0.5358 | 0.4107 | 0.8438   |
| 0.3391        | 6.0   | 1176  | 0.6615          | 0.3506    | 0.5547 | 0.4297 | 0.8386   |
| 0.3391        | 7.0   | 1372  | 0.6662          | 0.3534    | 0.5651 | 0.4348 | 0.8322   |
| 0.1955        | 8.0   | 1568  | 0.6838          | 0.3834    | 0.5726 | 0.4593 | 0.8300   |
| 0.1955        | 9.0   | 1764  | 0.7898          | 0.3901    | 0.5774 | 0.4656 | 0.8336   |
| 0.1955        | 10.0  | 1960  | 0.7843          | 0.4179    | 0.5934 | 0.4904 | 0.8329   |
| 0.12          | 11.0  | 2156  | 0.7695          | 0.3794    | 0.5774 | 0.4579 | 0.8228   |
| 0.12          | 12.0  | 2352  | 0.7917          | 0.4077    | 0.5858 | 0.4808 | 0.8468   |
| 0.0816        | 13.0  | 2548  | 0.7947          | 0.4171    | 0.6075 | 0.4946 | 0.8453   |
| 0.0816        | 14.0  | 2744  | 0.9385          | 0.4403    | 0.6160 | 0.5136 | 0.8391   |
| 0.0816        | 15.0  | 2940  | 0.9491          | 0.4163    | 0.5868 | 0.4871 | 0.8316   |
| 0.0554        | 16.0  | 3136  | 0.9586          | 0.4696    | 0.6189 | 0.5340 | 0.8373   |
| 0.0554        | 17.0  | 3332  | 0.9221          | 0.4565    | 0.6038 | 0.5199 | 0.8478   |
| 0.0387        | 18.0  | 3528  | 0.9156          | 0.4600    | 0.6245 | 0.5298 | 0.8486   |
| 0.0387        | 19.0  | 3724  | 0.9759          | 0.4587    | 0.6132 | 0.5248 | 0.8392   |
| 0.0387        | 20.0  | 3920  | 0.9874          | 0.4636    | 0.6075 | 0.5259 | 0.8406   |
| 0.0302        | 21.0  | 4116  | 1.0031          | 0.4697    | 0.6075 | 0.5298 | 0.8477   |
| 0.0302        | 22.0  | 4312  | 0.9735          | 0.4897    | 0.6292 | 0.5508 | 0.8523   |
| 0.0216        | 23.0  | 4508  | 1.0142          | 0.4893    | 0.6255 | 0.5491 | 0.8481   |
| 0.0216        | 24.0  | 4704  | 1.0030          | 0.4761    | 0.6387 | 0.5455 | 0.8540   |
| 0.0216        | 25.0  | 4900  | 1.0644          | 0.4745    | 0.6132 | 0.5350 | 0.8447   |
| 0.0195        | 26.0  | 5096  | 1.0565          | 0.4694    | 0.6217 | 0.5349 | 0.8441   |
| 0.0195        | 27.0  | 5292  | 1.0729          | 0.4781    | 0.6189 | 0.5395 | 0.8488   |
| 0.0195        | 28.0  | 5488  | 1.0865          | 0.4586    | 0.6274 | 0.5299 | 0.8420   |
| 0.0141        | 29.0  | 5684  | 1.1745          | 0.4805    | 0.6151 | 0.5395 | 0.8412   |
| 0.0141        | 30.0  | 5880  | 1.1908          | 0.4657    | 0.6217 | 0.5325 | 0.8378   |
| 0.0124        | 31.0  | 6076  | 1.2164          | 0.5062    | 0.6160 | 0.5557 | 0.8455   |
| 0.0124        | 32.0  | 6272  | 1.1651          | 0.4480    | 0.6217 | 0.5207 | 0.8365   |
| 0.0124        | 33.0  | 6468  | 1.1851          | 0.4700    | 0.6217 | 0.5353 | 0.8383   |
| 0.0105        | 34.0  | 6664  | 1.1538          | 0.4836    | 0.6274 | 0.5462 | 0.8506   |
| 0.0105        | 35.0  | 6860  | 1.2399          | 0.4739    | 0.6349 | 0.5427 | 0.8417   |
| 0.0093        | 36.0  | 7056  | 1.1659          | 0.4920    | 0.6387 | 0.5558 | 0.8450   |
| 0.0093        | 37.0  | 7252  | 1.1778          | 0.4955    | 0.6283 | 0.5541 | 0.8518   |
| 0.0093        | 38.0  | 7448  | 1.2633          | 0.4958    | 0.6179 | 0.5502 | 0.8432   |
| 0.0075        | 39.0  | 7644  | 1.1656          | 0.4960    | 0.6443 | 0.5605 | 0.8490   |
| 0.0075        | 40.0  | 7840  | 1.2003          | 0.4876    | 0.6292 | 0.5494 | 0.8479   |
| 0.0063        | 41.0  | 8036  | 1.2807          | 0.4828    | 0.6349 | 0.5485 | 0.8405   |
| 0.0063        | 42.0  | 8232  | 1.2237          | 0.5130    | 0.6330 | 0.5667 | 0.8528   |
| 0.0063        | 43.0  | 8428  | 1.2233          | 0.4812    | 0.6406 | 0.5496 | 0.8502   |
| 0.0047        | 44.0  | 8624  | 1.2412          | 0.4746    | 0.6179 | 0.5369 | 0.8467   |
| 0.0047        | 45.0  | 8820  | 1.2988          | 0.4985    | 0.6377 | 0.5596 | 0.8470   |
| 0.0049        | 46.0  | 9016  | 1.3227          | 0.4944    | 0.6264 | 0.5526 | 0.8474   |
| 0.0049        | 47.0  | 9212  | 1.3627          | 0.5054    | 0.6226 | 0.5579 | 0.8481   |
| 0.0049        | 48.0  | 9408  | 1.3941          | 0.5169    | 0.6208 | 0.5641 | 0.8404   |
| 0.005         | 49.0  | 9604  | 1.3395          | 0.5108    | 0.6264 | 0.5627 | 0.8457   |
| 0.005         | 50.0  | 9800  | 1.2560          | 0.5027    | 0.6208 | 0.5555 | 0.8517   |
| 0.005         | 51.0  | 9996  | 1.3470          | 0.4715    | 0.6160 | 0.5342 | 0.8438   |
| 0.005         | 52.0  | 10192 | 1.2791          | 0.5109    | 0.6208 | 0.5605 | 0.8517   |
| 0.005         | 53.0  | 10388 | 1.3045          | 0.4788    | 0.6274 | 0.5431 | 0.8480   |
| 0.0042        | 54.0  | 10584 | 1.3052          | 0.4955    | 0.6292 | 0.5544 | 0.8466   |
| 0.0042        | 55.0  | 10780 | 1.3140          | 0.5248    | 0.6292 | 0.5723 | 0.8503   |
| 0.0042        | 56.0  | 10976 | 1.2651          | 0.4776    | 0.6236 | 0.5409 | 0.8445   |
| 0.0045        | 57.0  | 11172 | 1.2664          | 0.4871    | 0.6255 | 0.5477 | 0.8489   |
| 0.0045        | 58.0  | 11368 | 1.3141          | 0.4974    | 0.6226 | 0.5530 | 0.8485   |
| 0.0018        | 59.0  | 11564 | 1.3525          | 0.5123    | 0.6274 | 0.5640 | 0.8460   |
| 0.0018        | 60.0  | 11760 | 1.3694          | 0.5188    | 0.6368 | 0.5718 | 0.8496   |
| 0.0018        | 61.0  | 11956 | 1.3892          | 0.5219    | 0.6292 | 0.5706 | 0.8440   |
| 0.0031        | 62.0  | 12152 | 1.3371          | 0.4951    | 0.6208 | 0.5509 | 0.8475   |
| 0.0031        | 63.0  | 12348 | 1.3313          | 0.5173    | 0.6349 | 0.5701 | 0.8554   |
| 0.002         | 64.0  | 12544 | 1.3916          | 0.5246    | 0.6349 | 0.5745 | 0.8503   |
| 0.002         | 65.0  | 12740 | 1.3874          | 0.5274    | 0.6358 | 0.5766 | 0.8490   |
| 0.002         | 66.0  | 12936 | 1.3903          | 0.4970    | 0.6292 | 0.5554 | 0.8459   |
| 0.0026        | 67.0  | 13132 | 1.3595          | 0.5090    | 0.6406 | 0.5673 | 0.8480   |
| 0.0026        | 68.0  | 13328 | 1.3849          | 0.5019    | 0.6368 | 0.5613 | 0.8478   |
| 0.0026        | 69.0  | 13524 | 1.3434          | 0.5148    | 0.6396 | 0.5705 | 0.8550   |
| 0.0026        | 70.0  | 13720 | 1.3593          | 0.5402    | 0.6396 | 0.5857 | 0.8561   |
| 0.0026        | 71.0  | 13916 | 1.3833          | 0.5227    | 0.6406 | 0.5757 | 0.8503   |
| 0.0014        | 72.0  | 14112 | 1.3807          | 0.4930    | 0.6283 | 0.5525 | 0.8464   |
| 0.0014        | 73.0  | 14308 | 1.4330          | 0.5060    | 0.6330 | 0.5624 | 0.8478   |
| 0.0009        | 74.0  | 14504 | 1.3308          | 0.5236    | 0.6274 | 0.5708 | 0.8603   |
| 0.0009        | 75.0  | 14700 | 1.3397          | 0.4837    | 0.6170 | 0.5423 | 0.8515   |
| 0.0009        | 76.0  | 14896 | 1.3821          | 0.5008    | 0.6245 | 0.5558 | 0.8481   |
| 0.0015        | 77.0  | 15092 | 1.3438          | 0.5030    | 0.6349 | 0.5613 | 0.8502   |
| 0.0015        | 78.0  | 15288 | 1.3522          | 0.5011    | 0.6208 | 0.5546 | 0.8476   |
| 0.0015        | 79.0  | 15484 | 1.3951          | 0.5134    | 0.6311 | 0.5662 | 0.8528   |
| 0.0008        | 80.0  | 15680 | 1.3744          | 0.5126    | 0.6330 | 0.5665 | 0.8510   |
| 0.0008        | 81.0  | 15876 | 1.4252          | 0.4864    | 0.6245 | 0.5469 | 0.8441   |
| 0.0006        | 82.0  | 16072 | 1.4555          | 0.5050    | 0.6255 | 0.5588 | 0.8445   |
| 0.0006        | 83.0  | 16268 | 1.4168          | 0.5107    | 0.6302 | 0.5642 | 0.8492   |
| 0.0006        | 84.0  | 16464 | 1.4010          | 0.4915    | 0.6302 | 0.5523 | 0.8493   |
| 0.0004        | 85.0  | 16660 | 1.3800          | 0.5161    | 0.6340 | 0.5690 | 0.8554   |
| 0.0004        | 86.0  | 16856 | 1.4098          | 0.5083    | 0.6321 | 0.5635 | 0.8520   |
| 0.0004        | 87.0  | 17052 | 1.3664          | 0.5122    | 0.6340 | 0.5666 | 0.8545   |
| 0.0004        | 88.0  | 17248 | 1.3677          | 0.5325    | 0.6415 | 0.5819 | 0.8515   |
| 0.0004        | 89.0  | 17444 | 1.4179          | 0.4912    | 0.6321 | 0.5528 | 0.8504   |
| 0.0012        | 90.0  | 17640 | 1.3850          | 0.5007    | 0.6340 | 0.5595 | 0.8533   |
| 0.0012        | 91.0  | 17836 | 1.4313          | 0.4937    | 0.6302 | 0.5537 | 0.8477   |
| 0.001         | 92.0  | 18032 | 1.4223          | 0.5056    | 0.6415 | 0.5655 | 0.8502   |
| 0.001         | 93.0  | 18228 | 1.4322          | 0.5030    | 0.6406 | 0.5635 | 0.8500   |
| 0.001         | 94.0  | 18424 | 1.4387          | 0.5098    | 0.6406 | 0.5677 | 0.8491   |
| 0.0002        | 95.0  | 18620 | 1.4464          | 0.5188    | 0.6377 | 0.5722 | 0.8501   |
| 0.0002        | 96.0  | 18816 | 1.4219          | 0.5208    | 0.6387 | 0.5737 | 0.8518   |
| 0.0011        | 97.0  | 19012 | 1.4226          | 0.5141    | 0.6368 | 0.5689 | 0.8513   |
| 0.0011        | 98.0  | 19208 | 1.4335          | 0.5195    | 0.6396 | 0.5734 | 0.8516   |
| 0.0011        | 99.0  | 19404 | 1.4271          | 0.5152    | 0.6396 | 0.5707 | 0.8528   |
| 0.0006        | 100.0 | 19600 | 1.4271          | 0.5208    | 0.6368 | 0.5730 | 0.8522   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "longformer-base-4096-bne-es-finetuned", "results": []}]}
transformers pytorch longformer token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #longformer #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

longformer-base-4096-bne-es-finetuned
=====================================


This model is a fine-tuned version of joheras/longformer-base-4096-bne-es-finetuned-clinais on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4271
* Precision: 0.5208
* Recall: 0.6368
* F1: 0.5730
* Accuracy: 0.8522


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
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* 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
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #longformer #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #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
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #longformer #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #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
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3