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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: clinico-xlm-roberta-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
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