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# clinico-xlm-roberta-finetuned

This model is a fine-tuned version of [joheras/xlm-roberta-base-finetuned-clinais](https://huggingface.co/joheras/xlm-roberta-base-finetuned-clinais) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1866
- Precision: 0.4629
- Recall: 0.6281
- F1: 0.5330
- Accuracy: 0.8501

## 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: 32
- eval_batch_size: 32
- 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   | 25   | 1.2657          | 0.0046    | 0.0103 | 0.0064 | 0.5444   |
| No log        | 2.0   | 50   | 0.7933          | 0.1430    | 0.2609 | 0.1848 | 0.7711   |
| No log        | 3.0   | 75   | 0.6467          | 0.2741    | 0.4325 | 0.3356 | 0.8061   |
| No log        | 4.0   | 100  | 0.5961          | 0.3151    | 0.5217 | 0.3929 | 0.8233   |
| No log        | 5.0   | 125  | 0.5628          | 0.3288    | 0.5217 | 0.4034 | 0.8289   |
| No log        | 6.0   | 150  | 0.5540          | 0.2884    | 0.4920 | 0.3636 | 0.8377   |
| No log        | 7.0   | 175  | 0.5475          | 0.2960    | 0.4954 | 0.3706 | 0.8381   |
| No log        | 8.0   | 200  | 0.6013          | 0.3034    | 0.5297 | 0.3858 | 0.8347   |
| No log        | 9.0   | 225  | 0.6026          | 0.2989    | 0.5297 | 0.3822 | 0.8368   |
| No log        | 10.0  | 250  | 0.6055          | 0.3352    | 0.5366 | 0.4127 | 0.8422   |
| No log        | 11.0  | 275  | 0.6757          | 0.2982    | 0.5275 | 0.3810 | 0.8385   |
| No log        | 12.0  | 300  | 0.6287          | 0.3135    | 0.5355 | 0.3954 | 0.8464   |
| No log        | 13.0  | 325  | 0.7429          | 0.3441    | 0.5492 | 0.4231 | 0.8402   |
| No log        | 14.0  | 350  | 0.6883          | 0.3203    | 0.5538 | 0.4059 | 0.8491   |
| No log        | 15.0  | 375  | 0.7311          | 0.3550    | 0.5698 | 0.4374 | 0.8427   |
| No log        | 16.0  | 400  | 0.7084          | 0.3518    | 0.5595 | 0.4320 | 0.8481   |
| No log        | 17.0  | 425  | 0.7104          | 0.3545    | 0.5629 | 0.4350 | 0.8533   |
| No log        | 18.0  | 450  | 0.7958          | 0.3572    | 0.5709 | 0.4395 | 0.8381   |
| No log        | 19.0  | 475  | 0.7453          | 0.3616    | 0.5755 | 0.4442 | 0.8516   |
| 0.3605        | 20.0  | 500  | 0.7714          | 0.3573    | 0.5744 | 0.4405 | 0.8430   |
| 0.3605        | 21.0  | 525  | 0.8162          | 0.3664    | 0.5744 | 0.4474 | 0.8469   |
| 0.3605        | 22.0  | 550  | 0.7999          | 0.3711    | 0.5847 | 0.4540 | 0.8527   |
| 0.3605        | 23.0  | 575  | 0.8143          | 0.3968    | 0.5938 | 0.4757 | 0.8537   |
| 0.3605        | 24.0  | 600  | 0.8394          | 0.4078    | 0.5892 | 0.4820 | 0.8516   |
| 0.3605        | 25.0  | 625  | 0.8772          | 0.3778    | 0.5675 | 0.4536 | 0.8397   |
| 0.3605        | 26.0  | 650  | 0.8670          | 0.3991    | 0.6178 | 0.4850 | 0.8549   |
| 0.3605        | 27.0  | 675  | 0.8739          | 0.3886    | 0.5904 | 0.4687 | 0.8491   |
| 0.3605        | 28.0  | 700  | 0.9461          | 0.4081    | 0.5973 | 0.4849 | 0.8447   |
| 0.3605        | 29.0  | 725  | 0.9134          | 0.4267    | 0.6064 | 0.5009 | 0.8448   |
| 0.3605        | 30.0  | 750  | 0.9127          | 0.4057    | 0.5984 | 0.4836 | 0.8440   |
| 0.3605        | 31.0  | 775  | 0.9738          | 0.4129    | 0.5995 | 0.4890 | 0.8435   |
| 0.3605        | 32.0  | 800  | 1.0001          | 0.4074    | 0.5892 | 0.4818 | 0.8442   |
| 0.3605        | 33.0  | 825  | 0.9532          | 0.4133    | 0.6030 | 0.4905 | 0.8470   |
| 0.3605        | 34.0  | 850  | 0.9532          | 0.4080    | 0.6041 | 0.4871 | 0.8481   |
| 0.3605        | 35.0  | 875  | 0.9876          | 0.4108    | 0.6087 | 0.4905 | 0.8483   |
| 0.3605        | 36.0  | 900  | 0.9456          | 0.4219    | 0.6247 | 0.5037 | 0.8521   |
| 0.3605        | 37.0  | 925  | 0.9513          | 0.4180    | 0.6121 | 0.4968 | 0.8468   |
| 0.3605        | 38.0  | 950  | 0.9905          | 0.4120    | 0.6110 | 0.4922 | 0.8506   |
| 0.3605        | 39.0  | 975  | 0.9983          | 0.4365    | 0.6247 | 0.5139 | 0.8522   |
| 0.0271        | 40.0  | 1000 | 1.0220          | 0.4224    | 0.6076 | 0.4984 | 0.8480   |
| 0.0271        | 41.0  | 1025 | 1.0323          | 0.4114    | 0.6110 | 0.4917 | 0.8474   |
| 0.0271        | 42.0  | 1050 | 1.0651          | 0.4266    | 0.6121 | 0.5028 | 0.8482   |
| 0.0271        | 43.0  | 1075 | 1.0778          | 0.4101    | 0.5927 | 0.4848 | 0.8534   |
| 0.0271        | 44.0  | 1100 | 1.0190          | 0.4216    | 0.6087 | 0.4981 | 0.8469   |
| 0.0271        | 45.0  | 1125 | 1.0374          | 0.4245    | 0.6144 | 0.5021 | 0.8544   |
| 0.0271        | 46.0  | 1150 | 1.0792          | 0.4383    | 0.6018 | 0.5072 | 0.8518   |
| 0.0271        | 47.0  | 1175 | 1.0888          | 0.4267    | 0.6190 | 0.5051 | 0.8478   |
| 0.0271        | 48.0  | 1200 | 1.1022          | 0.4498    | 0.6156 | 0.5198 | 0.8490   |
| 0.0271        | 49.0  | 1225 | 1.1646          | 0.4398    | 0.6064 | 0.5099 | 0.8453   |
| 0.0271        | 50.0  | 1250 | 1.1448          | 0.4505    | 0.6087 | 0.5178 | 0.8478   |
| 0.0271        | 51.0  | 1275 | 1.1288          | 0.4388    | 0.6110 | 0.5108 | 0.8455   |
| 0.0271        | 52.0  | 1300 | 1.1077          | 0.4579    | 0.6224 | 0.5276 | 0.8478   |
| 0.0271        | 53.0  | 1325 | 1.0931          | 0.4373    | 0.6064 | 0.5081 | 0.8465   |
| 0.0271        | 54.0  | 1350 | 1.1044          | 0.4478    | 0.6087 | 0.5160 | 0.8471   |
| 0.0271        | 55.0  | 1375 | 1.0895          | 0.4343    | 0.6087 | 0.5069 | 0.8500   |
| 0.0271        | 56.0  | 1400 | 1.0768          | 0.4501    | 0.6144 | 0.5196 | 0.8532   |
| 0.0271        | 57.0  | 1425 | 1.1164          | 0.4356    | 0.6190 | 0.5113 | 0.8510   |
| 0.0271        | 58.0  | 1450 | 1.1378          | 0.4507    | 0.6167 | 0.5208 | 0.8505   |
| 0.0271        | 59.0  | 1475 | 1.1510          | 0.4583    | 0.6156 | 0.5254 | 0.8500   |
| 0.0063        | 60.0  | 1500 | 1.1126          | 0.4654    | 0.6224 | 0.5326 | 0.8514   |
| 0.0063        | 61.0  | 1525 | 1.1535          | 0.4548    | 0.6156 | 0.5231 | 0.8515   |
| 0.0063        | 62.0  | 1550 | 1.1362          | 0.4535    | 0.6247 | 0.5255 | 0.8505   |
| 0.0063        | 63.0  | 1575 | 1.1321          | 0.4723    | 0.6247 | 0.5379 | 0.8546   |
| 0.0063        | 64.0  | 1600 | 1.0995          | 0.4626    | 0.6304 | 0.5337 | 0.8561   |
| 0.0063        | 65.0  | 1625 | 1.1263          | 0.4546    | 0.6190 | 0.5242 | 0.8498   |
| 0.0063        | 66.0  | 1650 | 1.1251          | 0.4712    | 0.6270 | 0.5380 | 0.8549   |
| 0.0063        | 67.0  | 1675 | 1.1592          | 0.4745    | 0.6281 | 0.5406 | 0.8501   |
| 0.0063        | 68.0  | 1700 | 1.1552          | 0.4571    | 0.6281 | 0.5292 | 0.8514   |
| 0.0063        | 69.0  | 1725 | 1.1602          | 0.4618    | 0.6224 | 0.5302 | 0.8520   |
| 0.0063        | 70.0  | 1750 | 1.1631          | 0.4669    | 0.6304 | 0.5365 | 0.8527   |
| 0.0063        | 71.0  | 1775 | 1.1784          | 0.4824    | 0.6259 | 0.5448 | 0.8487   |
| 0.0063        | 72.0  | 1800 | 1.1779          | 0.4681    | 0.6213 | 0.5339 | 0.8527   |
| 0.0063        | 73.0  | 1825 | 1.1656          | 0.4478    | 0.6236 | 0.5213 | 0.8531   |
| 0.0063        | 74.0  | 1850 | 1.1743          | 0.4620    | 0.6190 | 0.5291 | 0.8528   |
| 0.0063        | 75.0  | 1875 | 1.1623          | 0.4529    | 0.6270 | 0.5259 | 0.8520   |
| 0.0063        | 76.0  | 1900 | 1.1597          | 0.4831    | 0.6201 | 0.5431 | 0.8507   |
| 0.0063        | 77.0  | 1925 | 1.1603          | 0.4743    | 0.6236 | 0.5388 | 0.8520   |
| 0.0063        | 78.0  | 1950 | 1.1551          | 0.4505    | 0.6190 | 0.5214 | 0.8500   |
| 0.0063        | 79.0  | 1975 | 1.1740          | 0.4772    | 0.6213 | 0.5398 | 0.8511   |
| 0.0026        | 80.0  | 2000 | 1.1463          | 0.4706    | 0.6224 | 0.5360 | 0.8519   |
| 0.0026        | 81.0  | 2025 | 1.1757          | 0.4603    | 0.6167 | 0.5271 | 0.8472   |
| 0.0026        | 82.0  | 2050 | 1.1754          | 0.4541    | 0.6224 | 0.5251 | 0.8457   |
| 0.0026        | 83.0  | 2075 | 1.1713          | 0.4588    | 0.6178 | 0.5266 | 0.8476   |
| 0.0026        | 84.0  | 2100 | 1.2023          | 0.4631    | 0.6247 | 0.5319 | 0.8473   |
| 0.0026        | 85.0  | 2125 | 1.1819          | 0.4841    | 0.6259 | 0.5459 | 0.8471   |
| 0.0026        | 86.0  | 2150 | 1.1878          | 0.4611    | 0.6236 | 0.5302 | 0.8470   |
| 0.0026        | 87.0  | 2175 | 1.1827          | 0.4694    | 0.6236 | 0.5356 | 0.8485   |
| 0.0026        | 88.0  | 2200 | 1.1787          | 0.4552    | 0.6213 | 0.5254 | 0.8506   |
| 0.0026        | 89.0  | 2225 | 1.1811          | 0.4762    | 0.6293 | 0.5421 | 0.8488   |
| 0.0026        | 90.0  | 2250 | 1.1849          | 0.4573    | 0.6247 | 0.5280 | 0.8493   |
| 0.0026        | 91.0  | 2275 | 1.1779          | 0.4505    | 0.6247 | 0.5235 | 0.8502   |
| 0.0026        | 92.0  | 2300 | 1.2042          | 0.4672    | 0.6201 | 0.5329 | 0.8493   |
| 0.0026        | 93.0  | 2325 | 1.1955          | 0.4712    | 0.6270 | 0.5380 | 0.8501   |
| 0.0026        | 94.0  | 2350 | 1.1950          | 0.4696    | 0.6281 | 0.5374 | 0.8503   |
| 0.0026        | 95.0  | 2375 | 1.1958          | 0.4769    | 0.6270 | 0.5418 | 0.8489   |
| 0.0026        | 96.0  | 2400 | 1.1819          | 0.4564    | 0.6281 | 0.5286 | 0.8496   |
| 0.0026        | 97.0  | 2425 | 1.1853          | 0.4677    | 0.6304 | 0.5370 | 0.8501   |
| 0.0026        | 98.0  | 2450 | 1.1822          | 0.4637    | 0.6281 | 0.5335 | 0.8501   |
| 0.0026        | 99.0  | 2475 | 1.1841          | 0.4571    | 0.6281 | 0.5292 | 0.8498   |
| 0.0014        | 100.0 | 2500 | 1.1866          | 0.4629    | 0.6281 | 0.5330 | 0.8501   |


### 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-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-finetuned
=============================


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


* Loss: 1.1866
* Precision: 0.4629
* Recall: 0.6281
* F1: 0.5330
* Accuracy: 0.8501


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: 32
* eval\_batch\_size: 32
* 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 4.27.0.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: 32
* eval\_batch\_size: 32
* 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 4.27.0.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: 32
* eval\_batch\_size: 32
* 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 4.27.0.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: 32
* eval\_batch\_size: 32
* 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 4.27.0.dev0
* Pytorch 1.13.0
* Datasets 2.8.0
* Tokenizers 0.12.1