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# clinico-xlm-roberta-large-finetuned

This model is a fine-tuned version of [joheras/xlm-roberta-large-finetuned-clinais](https://huggingface.co/joheras/xlm-roberta-large-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3788
- Precision: 0.5418
- Recall: 0.6453
- F1: 0.5890
- Accuracy: 0.8708

## 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: 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 49   | 0.5993          | 0.2242    | 0.4577 | 0.3010 | 0.8168   |
| No log        | 2.0   | 98   | 0.5762          | 0.2365    | 0.4668 | 0.3140 | 0.8280   |
| No log        | 3.0   | 147  | 0.5627          | 0.2460    | 0.4989 | 0.3296 | 0.8326   |
| No log        | 4.0   | 196  | 0.5260          | 0.3029    | 0.5435 | 0.3890 | 0.8534   |
| No log        | 5.0   | 245  | 0.5838          | 0.3058    | 0.5343 | 0.3890 | 0.8559   |
| No log        | 6.0   | 294  | 0.6085          | 0.3369    | 0.5686 | 0.4232 | 0.8545   |
| No log        | 7.0   | 343  | 0.6481          | 0.3532    | 0.5824 | 0.4397 | 0.8591   |
| No log        | 8.0   | 392  | 0.6809          | 0.3523    | 0.5744 | 0.4367 | 0.8580   |
| No log        | 9.0   | 441  | 0.8040          | 0.3864    | 0.5778 | 0.4631 | 0.8568   |
| No log        | 10.0  | 490  | 0.7505          | 0.3775    | 0.5870 | 0.4595 | 0.8635   |
| 0.3143        | 11.0  | 539  | 0.8028          | 0.4350    | 0.6007 | 0.5046 | 0.8610   |
| 0.3143        | 12.0  | 588  | 0.8103          | 0.4253    | 0.6190 | 0.5042 | 0.8672   |
| 0.3143        | 13.0  | 637  | 0.8302          | 0.4506    | 0.6362 | 0.5275 | 0.8637   |
| 0.3143        | 14.0  | 686  | 0.9385          | 0.4581    | 0.6247 | 0.5286 | 0.8583   |
| 0.3143        | 15.0  | 735  | 0.9407          | 0.4304    | 0.6156 | 0.5066 | 0.8639   |
| 0.3143        | 16.0  | 784  | 0.9105          | 0.4421    | 0.6201 | 0.5162 | 0.8679   |
| 0.3143        | 17.0  | 833  | 0.9616          | 0.4691    | 0.6247 | 0.5358 | 0.8654   |
| 0.3143        | 18.0  | 882  | 0.9695          | 0.4799    | 0.6281 | 0.5441 | 0.8680   |
| 0.3143        | 19.0  | 931  | 1.0195          | 0.4996    | 0.6396 | 0.5610 | 0.8735   |
| 0.3143        | 20.0  | 980  | 1.0073          | 0.4670    | 0.6224 | 0.5336 | 0.8642   |
| 0.0259        | 21.0  | 1029 | 1.0354          | 0.4783    | 0.6316 | 0.5444 | 0.8673   |
| 0.0259        | 22.0  | 1078 | 1.1327          | 0.5258    | 0.6419 | 0.5781 | 0.8646   |
| 0.0259        | 23.0  | 1127 | 1.0605          | 0.5055    | 0.6281 | 0.5602 | 0.8668   |
| 0.0259        | 24.0  | 1176 | 1.0120          | 0.5158    | 0.6350 | 0.5692 | 0.8657   |
| 0.0259        | 25.0  | 1225 | 1.0205          | 0.4920    | 0.6339 | 0.5540 | 0.8729   |
| 0.0259        | 26.0  | 1274 | 1.0583          | 0.4995    | 0.6259 | 0.5556 | 0.8688   |
| 0.0259        | 27.0  | 1323 | 1.1157          | 0.5066    | 0.6545 | 0.5711 | 0.8698   |
| 0.0259        | 28.0  | 1372 | 1.1049          | 0.5048    | 0.6568 | 0.5709 | 0.8694   |
| 0.0259        | 29.0  | 1421 | 1.1167          | 0.4978    | 0.6487 | 0.5633 | 0.8685   |
| 0.0259        | 30.0  | 1470 | 1.1614          | 0.5       | 0.6625 | 0.5699 | 0.8644   |
| 0.0062        | 31.0  | 1519 | 1.1521          | 0.4991    | 0.6453 | 0.5629 | 0.8647   |
| 0.0062        | 32.0  | 1568 | 1.1951          | 0.4938    | 0.6419 | 0.5582 | 0.8661   |
| 0.0062        | 33.0  | 1617 | 1.2044          | 0.4815    | 0.6419 | 0.5503 | 0.8676   |
| 0.0062        | 34.0  | 1666 | 1.1952          | 0.5242    | 0.6556 | 0.5826 | 0.8712   |
| 0.0062        | 35.0  | 1715 | 1.1598          | 0.5283    | 0.6625 | 0.5878 | 0.8768   |
| 0.0062        | 36.0  | 1764 | 1.1716          | 0.5221    | 0.6613 | 0.5835 | 0.8720   |
| 0.0062        | 37.0  | 1813 | 1.2127          | 0.5236    | 0.6465 | 0.5786 | 0.8707   |
| 0.0062        | 38.0  | 1862 | 1.2747          | 0.5259    | 0.6499 | 0.5814 | 0.8692   |
| 0.0062        | 39.0  | 1911 | 1.2397          | 0.5363    | 0.6590 | 0.5914 | 0.8676   |
| 0.0062        | 40.0  | 1960 | 1.2358          | 0.5477    | 0.6568 | 0.5973 | 0.8746   |
| 0.0014        | 41.0  | 2009 | 1.2332          | 0.5367    | 0.6602 | 0.5921 | 0.8745   |
| 0.0014        | 42.0  | 2058 | 1.2239          | 0.5106    | 0.6602 | 0.5758 | 0.8685   |
| 0.0014        | 43.0  | 2107 | 1.2163          | 0.5224    | 0.6533 | 0.5806 | 0.8679   |
| 0.0014        | 44.0  | 2156 | 1.2335          | 0.5349    | 0.6568 | 0.5896 | 0.8694   |
| 0.0014        | 45.0  | 2205 | 1.3374          | 0.5348    | 0.6236 | 0.5758 | 0.8680   |
| 0.0014        | 46.0  | 2254 | 1.2287          | 0.5417    | 0.6533 | 0.5923 | 0.8730   |
| 0.0014        | 47.0  | 2303 | 1.2268          | 0.5536    | 0.6796 | 0.6102 | 0.8789   |
| 0.0014        | 48.0  | 2352 | 1.2153          | 0.4974    | 0.6568 | 0.5661 | 0.8737   |
| 0.0014        | 49.0  | 2401 | 1.2180          | 0.5222    | 0.6590 | 0.5827 | 0.8747   |
| 0.0014        | 50.0  | 2450 | 1.2906          | 0.5500    | 0.6476 | 0.5949 | 0.8698   |
| 0.0014        | 51.0  | 2499 | 1.2547          | 0.5386    | 0.6384 | 0.5843 | 0.8686   |
| 0.0018        | 52.0  | 2548 | 1.2792          | 0.5307    | 0.6430 | 0.5815 | 0.8681   |
| 0.0018        | 53.0  | 2597 | 1.1972          | 0.5040    | 0.6510 | 0.5681 | 0.8705   |
| 0.0018        | 54.0  | 2646 | 1.2189          | 0.5215    | 0.6533 | 0.5800 | 0.8782   |
| 0.0018        | 55.0  | 2695 | 1.2239          | 0.5602    | 0.6602 | 0.6061 | 0.8789   |
| 0.0018        | 56.0  | 2744 | 1.2620          | 0.5410    | 0.6648 | 0.5965 | 0.8773   |
| 0.0018        | 57.0  | 2793 | 1.2828          | 0.5513    | 0.6522 | 0.5975 | 0.8747   |
| 0.0018        | 58.0  | 2842 | 1.2633          | 0.5518    | 0.6522 | 0.5978 | 0.8749   |
| 0.0018        | 59.0  | 2891 | 1.2619          | 0.5356    | 0.6796 | 0.5991 | 0.8738   |
| 0.0018        | 60.0  | 2940 | 1.2076          | 0.5385    | 0.6716 | 0.5978 | 0.8775   |
| 0.0018        | 61.0  | 2989 | 1.2996          | 0.5357    | 0.6442 | 0.5849 | 0.8686   |
| 0.0011        | 62.0  | 3038 | 1.2614          | 0.5483    | 0.6693 | 0.6028 | 0.8773   |
| 0.0011        | 63.0  | 3087 | 1.2713          | 0.5524    | 0.6579 | 0.6005 | 0.8757   |
| 0.0011        | 64.0  | 3136 | 1.2920          | 0.5550    | 0.6579 | 0.6021 | 0.8739   |
| 0.0011        | 65.0  | 3185 | 1.3319          | 0.5623    | 0.6716 | 0.6121 | 0.8713   |
| 0.0011        | 66.0  | 3234 | 1.3345          | 0.5433    | 0.6533 | 0.5932 | 0.8720   |
| 0.0011        | 67.0  | 3283 | 1.3146          | 0.5305    | 0.6465 | 0.5828 | 0.8657   |
| 0.0011        | 68.0  | 3332 | 1.3354          | 0.5452    | 0.6556 | 0.5953 | 0.8691   |
| 0.0011        | 69.0  | 3381 | 1.3474          | 0.5519    | 0.6693 | 0.6050 | 0.8759   |
| 0.0011        | 70.0  | 3430 | 1.3498          | 0.5403    | 0.6590 | 0.5938 | 0.8686   |
| 0.0011        | 71.0  | 3479 | 1.3340          | 0.5387    | 0.6602 | 0.5933 | 0.8749   |
| 0.0005        | 72.0  | 3528 | 1.3475          | 0.5615    | 0.6636 | 0.6083 | 0.8745   |
| 0.0005        | 73.0  | 3577 | 1.3530          | 0.5425    | 0.6648 | 0.5974 | 0.8746   |
| 0.0005        | 74.0  | 3626 | 1.3494          | 0.5491    | 0.6648 | 0.6014 | 0.8738   |
| 0.0005        | 75.0  | 3675 | 1.3368          | 0.5620    | 0.6590 | 0.6066 | 0.8749   |
| 0.0005        | 76.0  | 3724 | 1.3382          | 0.5467    | 0.6625 | 0.5991 | 0.8752   |
| 0.0005        | 77.0  | 3773 | 1.3486          | 0.5377    | 0.6533 | 0.5899 | 0.8759   |
| 0.0005        | 78.0  | 3822 | 1.3485          | 0.5483    | 0.6499 | 0.5948 | 0.8731   |
| 0.0005        | 79.0  | 3871 | 1.3512          | 0.5340    | 0.6556 | 0.5886 | 0.8751   |
| 0.0005        | 80.0  | 3920 | 1.3486          | 0.5513    | 0.6636 | 0.6023 | 0.8772   |
| 0.0005        | 81.0  | 3969 | 1.3530          | 0.5481    | 0.6579 | 0.5980 | 0.8772   |
| 0.0001        | 82.0  | 4018 | 1.3940          | 0.5536    | 0.6499 | 0.5979 | 0.8751   |
| 0.0001        | 83.0  | 4067 | 1.3657          | 0.5296    | 0.6453 | 0.5817 | 0.8742   |
| 0.0001        | 84.0  | 4116 | 1.3538          | 0.5412    | 0.6384 | 0.5858 | 0.8719   |
| 0.0001        | 85.0  | 4165 | 1.3550          | 0.5418    | 0.6373 | 0.5857 | 0.8693   |
| 0.0001        | 86.0  | 4214 | 1.3810          | 0.5187    | 0.6362 | 0.5714 | 0.8685   |
| 0.0001        | 87.0  | 4263 | 1.3625          | 0.5370    | 0.6396 | 0.5838 | 0.8707   |
| 0.0001        | 88.0  | 4312 | 1.3605          | 0.5389    | 0.6419 | 0.5859 | 0.8712   |
| 0.0001        | 89.0  | 4361 | 1.3616          | 0.5388    | 0.6430 | 0.5863 | 0.8711   |
| 0.0001        | 90.0  | 4410 | 1.3560          | 0.5431    | 0.6419 | 0.5884 | 0.8719   |
| 0.0001        | 91.0  | 4459 | 1.3558          | 0.5399    | 0.6430 | 0.5869 | 0.8716   |
| 0.0001        | 92.0  | 4508 | 1.3586          | 0.5342    | 0.6430 | 0.5836 | 0.8717   |
| 0.0001        | 93.0  | 4557 | 1.3727          | 0.5349    | 0.6487 | 0.5863 | 0.8716   |
| 0.0001        | 94.0  | 4606 | 1.3810          | 0.5539    | 0.6465 | 0.5966 | 0.8707   |
| 0.0001        | 95.0  | 4655 | 1.3813          | 0.5540    | 0.6453 | 0.5962 | 0.8705   |
| 0.0001        | 96.0  | 4704 | 1.3879          | 0.5625    | 0.6487 | 0.6026 | 0.8699   |
| 0.0001        | 97.0  | 4753 | 1.3886          | 0.5614    | 0.6487 | 0.6019 | 0.8702   |
| 0.0001        | 98.0  | 4802 | 1.3811          | 0.5470    | 0.6453 | 0.5921 | 0.8704   |
| 0.0001        | 99.0  | 4851 | 1.3788          | 0.5407    | 0.6453 | 0.5884 | 0.8709   |
| 0.0001        | 100.0 | 4900 | 1.3788          | 0.5418    | 0.6453 | 0.5890 | 0.8708   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "clinico-xlm-roberta-large-finetuned", "results": []}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

clinico-xlm-roberta-large-finetuned
===================================


This model is a fine-tuned version of joheras/xlm-roberta-large-finetuned-clinais on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.3788
* Precision: 0.5418
* Recall: 0.6453
* F1: 0.5890
* Accuracy: 0.8708


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: 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: 100
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 100
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1