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# clinico-xlm-roberta-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3753
- Precision: 0.5315
- Recall: 0.6465
- F1: 0.5834
- Accuracy: 0.8688

## 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   | 0.6619          | 0.1116    | 0.3455 | 0.1687 | 0.8029   |
| No log        | 2.0   | 98   | 0.5662          | 0.2196    | 0.4497 | 0.2950 | 0.8296   |
| No log        | 3.0   | 147  | 0.4912          | 0.2585    | 0.5069 | 0.3423 | 0.8469   |
| No log        | 4.0   | 196  | 0.5450          | 0.3165    | 0.5538 | 0.4028 | 0.8522   |
| No log        | 5.0   | 245  | 0.5523          | 0.3185    | 0.5309 | 0.3981 | 0.8578   |
| No log        | 6.0   | 294  | 0.6129          | 0.3057    | 0.5561 | 0.3945 | 0.8449   |
| No log        | 7.0   | 343  | 0.5760          | 0.3295    | 0.5595 | 0.4148 | 0.8523   |
| No log        | 8.0   | 392  | 0.6572          | 0.3749    | 0.5950 | 0.4600 | 0.8657   |
| No log        | 9.0   | 441  | 0.7340          | 0.3695    | 0.5915 | 0.4549 | 0.8603   |
| No log        | 10.0  | 490  | 0.8306          | 0.3494    | 0.5572 | 0.4295 | 0.8558   |
| 0.3262        | 11.0  | 539  | 0.8389          | 0.3714    | 0.5732 | 0.4507 | 0.8599   |
| 0.3262        | 12.0  | 588  | 0.8278          | 0.3880    | 0.5767 | 0.4639 | 0.8479   |
| 0.3262        | 13.0  | 637  | 0.8057          | 0.4038    | 0.6076 | 0.4852 | 0.8660   |
| 0.3262        | 14.0  | 686  | 0.8489          | 0.3847    | 0.5915 | 0.4662 | 0.8619   |
| 0.3262        | 15.0  | 735  | 0.8954          | 0.3868    | 0.5961 | 0.4692 | 0.8594   |
| 0.3262        | 16.0  | 784  | 0.8951          | 0.3926    | 0.5835 | 0.4694 | 0.8594   |
| 0.3262        | 17.0  | 833  | 0.9715          | 0.4080    | 0.5961 | 0.4844 | 0.8625   |
| 0.3262        | 18.0  | 882  | 0.9600          | 0.4317    | 0.6144 | 0.5071 | 0.8652   |
| 0.3262        | 19.0  | 931  | 0.9335          | 0.4369    | 0.6224 | 0.5134 | 0.8682   |
| 0.3262        | 20.0  | 980  | 0.8988          | 0.4178    | 0.6110 | 0.4963 | 0.8656   |
| 0.0323        | 21.0  | 1029 | 1.0445          | 0.4410    | 0.6110 | 0.5122 | 0.8637   |
| 0.0323        | 22.0  | 1078 | 0.9596          | 0.5078    | 0.6339 | 0.5639 | 0.8680   |
| 0.0323        | 23.0  | 1127 | 1.0240          | 0.4810    | 0.6384 | 0.5487 | 0.8643   |
| 0.0323        | 24.0  | 1176 | 1.0528          | 0.5367    | 0.6613 | 0.5925 | 0.8667   |
| 0.0323        | 25.0  | 1225 | 1.0788          | 0.5128    | 0.6648 | 0.5790 | 0.8713   |
| 0.0323        | 26.0  | 1274 | 1.0661          | 0.5268    | 0.6533 | 0.5832 | 0.8729   |
| 0.0323        | 27.0  | 1323 | 1.1575          | 0.5276    | 0.6568 | 0.5851 | 0.8733   |
| 0.0323        | 28.0  | 1372 | 1.2267          | 0.4929    | 0.6350 | 0.5550 | 0.8553   |
| 0.0323        | 29.0  | 1421 | 1.0935          | 0.5187    | 0.6499 | 0.5769 | 0.8718   |
| 0.0323        | 30.0  | 1470 | 1.2093          | 0.5162    | 0.6556 | 0.5776 | 0.8676   |
| 0.0074        | 31.0  | 1519 | 1.1556          | 0.5227    | 0.6590 | 0.5830 | 0.8750   |
| 0.0074        | 32.0  | 1568 | 1.2110          | 0.5632    | 0.6579 | 0.6069 | 0.8685   |
| 0.0074        | 33.0  | 1617 | 1.2201          | 0.5273    | 0.6304 | 0.5743 | 0.8645   |
| 0.0074        | 34.0  | 1666 | 1.1884          | 0.5167    | 0.6533 | 0.5771 | 0.8692   |
| 0.0074        | 35.0  | 1715 | 1.2731          | 0.5125    | 0.6327 | 0.5663 | 0.8691   |
| 0.0074        | 36.0  | 1764 | 1.2366          | 0.5054    | 0.6396 | 0.5646 | 0.8622   |
| 0.0074        | 37.0  | 1813 | 1.2428          | 0.5257    | 0.6545 | 0.5831 | 0.8697   |
| 0.0074        | 38.0  | 1862 | 1.2853          | 0.5299    | 0.6281 | 0.5749 | 0.8612   |
| 0.0074        | 39.0  | 1911 | 1.2748          | 0.5260    | 0.6362 | 0.5759 | 0.8604   |
| 0.0074        | 40.0  | 1960 | 1.3006          | 0.5387    | 0.6533 | 0.5905 | 0.8625   |
| 0.0022        | 41.0  | 2009 | 1.3935          | 0.5217    | 0.6339 | 0.5723 | 0.8566   |
| 0.0022        | 42.0  | 2058 | 1.2644          | 0.5154    | 0.6510 | 0.5753 | 0.8646   |
| 0.0022        | 43.0  | 2107 | 1.3069          | 0.5160    | 0.6259 | 0.5657 | 0.8658   |
| 0.0022        | 44.0  | 2156 | 1.3047          | 0.5161    | 0.6419 | 0.5722 | 0.8665   |
| 0.0022        | 45.0  | 2205 | 1.3570          | 0.5352    | 0.6350 | 0.5808 | 0.8620   |
| 0.0022        | 46.0  | 2254 | 1.2924          | 0.5239    | 0.6384 | 0.5756 | 0.8662   |
| 0.0022        | 47.0  | 2303 | 1.3362          | 0.5247    | 0.6568 | 0.5833 | 0.8622   |
| 0.0022        | 48.0  | 2352 | 1.3201          | 0.5301    | 0.6545 | 0.5858 | 0.8651   |
| 0.0022        | 49.0  | 2401 | 1.3418          | 0.5318    | 0.6407 | 0.5812 | 0.8674   |
| 0.0022        | 50.0  | 2450 | 1.3468          | 0.5005    | 0.6304 | 0.5580 | 0.8658   |
| 0.0022        | 51.0  | 2499 | 1.4094          | 0.5403    | 0.6373 | 0.5848 | 0.8573   |
| 0.0011        | 52.0  | 2548 | 1.3697          | 0.5307    | 0.6430 | 0.5815 | 0.8648   |
| 0.0011        | 53.0  | 2597 | 1.3840          | 0.5519    | 0.6384 | 0.5920 | 0.8609   |
| 0.0011        | 54.0  | 2646 | 1.3421          | 0.5415    | 0.6487 | 0.5903 | 0.8660   |
| 0.0011        | 55.0  | 2695 | 1.3011          | 0.5416    | 0.6556 | 0.5932 | 0.8696   |
| 0.0011        | 56.0  | 2744 | 1.3487          | 0.5491    | 0.6522 | 0.5962 | 0.8672   |
| 0.0011        | 57.0  | 2793 | 1.3309          | 0.5627    | 0.6465 | 0.6017 | 0.8641   |
| 0.0011        | 58.0  | 2842 | 1.3432          | 0.5376    | 0.6384 | 0.5837 | 0.8658   |
| 0.0011        | 59.0  | 2891 | 1.3824          | 0.5547    | 0.6327 | 0.5911 | 0.8660   |
| 0.0011        | 60.0  | 2940 | 1.3315          | 0.5135    | 0.6327 | 0.5669 | 0.8639   |
| 0.0011        | 61.0  | 2989 | 1.3656          | 0.5272    | 0.6327 | 0.5751 | 0.8637   |
| 0.0009        | 62.0  | 3038 | 1.3466          | 0.5369    | 0.6327 | 0.5809 | 0.8626   |
| 0.0009        | 63.0  | 3087 | 1.3103          | 0.5198    | 0.6453 | 0.5758 | 0.8645   |
| 0.0009        | 64.0  | 3136 | 1.4302          | 0.5304    | 0.6396 | 0.5799 | 0.8559   |
| 0.0009        | 65.0  | 3185 | 1.4510          | 0.5350    | 0.6476 | 0.5859 | 0.8598   |
| 0.0009        | 66.0  | 3234 | 1.3478          | 0.5196    | 0.6384 | 0.5729 | 0.8656   |
| 0.0009        | 67.0  | 3283 | 1.4041          | 0.5436    | 0.6350 | 0.5858 | 0.8636   |
| 0.0009        | 68.0  | 3332 | 1.3659          | 0.5673    | 0.6362 | 0.5998 | 0.8702   |
| 0.0009        | 69.0  | 3381 | 1.3418          | 0.5473    | 0.6419 | 0.5908 | 0.8702   |
| 0.0009        | 70.0  | 3430 | 1.3634          | 0.5402    | 0.6384 | 0.5852 | 0.8657   |
| 0.0009        | 71.0  | 3479 | 1.4288          | 0.5523    | 0.6465 | 0.5957 | 0.8613   |
| 0.0008        | 72.0  | 3528 | 1.3958          | 0.5413    | 0.6304 | 0.5825 | 0.8643   |
| 0.0008        | 73.0  | 3577 | 1.4010          | 0.5344    | 0.6316 | 0.5789 | 0.8683   |
| 0.0008        | 74.0  | 3626 | 1.3712          | 0.5361    | 0.6453 | 0.5857 | 0.8663   |
| 0.0008        | 75.0  | 3675 | 1.3434          | 0.5325    | 0.6465 | 0.5840 | 0.8708   |
| 0.0008        | 76.0  | 3724 | 1.3502          | 0.5140    | 0.6304 | 0.5663 | 0.8682   |
| 0.0008        | 77.0  | 3773 | 1.3639          | 0.5330    | 0.6373 | 0.5805 | 0.8691   |
| 0.0008        | 78.0  | 3822 | 1.3515          | 0.5167    | 0.6373 | 0.5707 | 0.8697   |
| 0.0008        | 79.0  | 3871 | 1.3677          | 0.5228    | 0.6430 | 0.5767 | 0.8691   |
| 0.0008        | 80.0  | 3920 | 1.4069          | 0.5401    | 0.6396 | 0.5856 | 0.8672   |
| 0.0008        | 81.0  | 3969 | 1.3813          | 0.5307    | 0.6522 | 0.5852 | 0.8672   |
| 0.0002        | 82.0  | 4018 | 1.3773          | 0.5355    | 0.6476 | 0.5862 | 0.8678   |
| 0.0002        | 83.0  | 4067 | 1.4004          | 0.5279    | 0.6281 | 0.5737 | 0.8674   |
| 0.0002        | 84.0  | 4116 | 1.4027          | 0.5532    | 0.6487 | 0.5972 | 0.8696   |
| 0.0002        | 85.0  | 4165 | 1.3544          | 0.5351    | 0.6362 | 0.5813 | 0.8672   |
| 0.0002        | 86.0  | 4214 | 1.3582          | 0.5367    | 0.6362 | 0.5822 | 0.8664   |
| 0.0002        | 87.0  | 4263 | 1.3594          | 0.5300    | 0.6362 | 0.5783 | 0.8666   |
| 0.0002        | 88.0  | 4312 | 1.3737          | 0.5371    | 0.6384 | 0.5834 | 0.8690   |
| 0.0002        | 89.0  | 4361 | 1.3991          | 0.5368    | 0.6339 | 0.5813 | 0.8688   |
| 0.0002        | 90.0  | 4410 | 1.3819          | 0.5400    | 0.6407 | 0.5861 | 0.8679   |
| 0.0002        | 91.0  | 4459 | 1.3900          | 0.5495    | 0.6419 | 0.5921 | 0.8699   |
| 0.0001        | 92.0  | 4508 | 1.3890          | 0.5509    | 0.6442 | 0.5939 | 0.8703   |
| 0.0001        | 93.0  | 4557 | 1.3825          | 0.5336    | 0.6442 | 0.5837 | 0.8686   |
| 0.0001        | 94.0  | 4606 | 1.3821          | 0.5370    | 0.6476 | 0.5871 | 0.8686   |
| 0.0001        | 95.0  | 4655 | 1.3803          | 0.5437    | 0.6476 | 0.5911 | 0.8685   |
| 0.0001        | 96.0  | 4704 | 1.3747          | 0.5395    | 0.6487 | 0.5891 | 0.8688   |
| 0.0001        | 97.0  | 4753 | 1.3752          | 0.5223    | 0.6442 | 0.5768 | 0.8683   |
| 0.0001        | 98.0  | 4802 | 1.3775          | 0.5280    | 0.6465 | 0.5813 | 0.8685   |
| 0.0001        | 99.0  | 4851 | 1.3784          | 0.5300    | 0.6465 | 0.5825 | 0.8685   |
| 0.0001        | 100.0 | 4900 | 1.3753          | 0.5315    | 0.6465 | 0.5834 | 0.8688   |


### 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", "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
=========================


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


* Loss: 1.3753
* Precision: 0.5315
* Recall: 0.6465
* F1: 0.5834
* Accuracy: 0.8688


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