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# clinico-xlm-roberta

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2239
- Precision: 0.4333
- Recall: 0.5984
- F1: 0.5026
- Accuracy: 0.8446

## 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   | 1.4128          | 0.0015    | 0.0023 | 0.0018 | 0.5371   |
| No log        | 2.0   | 50   | 1.0675          | 0.0260    | 0.0595 | 0.0362 | 0.6283   |
| No log        | 3.0   | 75   | 0.9345          | 0.0456    | 0.1213 | 0.0663 | 0.6667   |
| No log        | 4.0   | 100  | 0.8709          | 0.0707    | 0.1465 | 0.0954 | 0.7060   |
| No log        | 5.0   | 125  | 0.8154          | 0.1112    | 0.2059 | 0.1444 | 0.7399   |
| No log        | 6.0   | 150  | 0.8001          | 0.1615    | 0.3066 | 0.2116 | 0.7347   |
| No log        | 7.0   | 175  | 0.6928          | 0.2129    | 0.3616 | 0.2680 | 0.7846   |
| No log        | 8.0   | 200  | 0.6576          | 0.2210    | 0.3753 | 0.2782 | 0.7988   |
| No log        | 9.0   | 225  | 0.6174          | 0.2263    | 0.4119 | 0.2921 | 0.8120   |
| No log        | 10.0  | 250  | 0.6232          | 0.2385    | 0.4268 | 0.3060 | 0.8150   |
| No log        | 11.0  | 275  | 0.6304          | 0.2528    | 0.4577 | 0.3257 | 0.8237   |
| No log        | 12.0  | 300  | 0.6562          | 0.2758    | 0.4714 | 0.3480 | 0.8204   |
| No log        | 13.0  | 325  | 0.6725          | 0.2726    | 0.4828 | 0.3485 | 0.8164   |
| No log        | 14.0  | 350  | 0.6959          | 0.2732    | 0.4943 | 0.3519 | 0.8216   |
| No log        | 15.0  | 375  | 0.6838          | 0.2919    | 0.5046 | 0.3698 | 0.8313   |
| No log        | 16.0  | 400  | 0.7033          | 0.3199    | 0.5252 | 0.3976 | 0.8307   |
| No log        | 17.0  | 425  | 0.7198          | 0.2848    | 0.4897 | 0.3601 | 0.8094   |
| No log        | 18.0  | 450  | 0.7319          | 0.3070    | 0.5149 | 0.3846 | 0.8293   |
| No log        | 19.0  | 475  | 0.7841          | 0.3184    | 0.5275 | 0.3971 | 0.8283   |
| 0.5744        | 20.0  | 500  | 0.8119          | 0.2995    | 0.5229 | 0.3808 | 0.8204   |
| 0.5744        | 21.0  | 525  | 0.7665          | 0.2914    | 0.5069 | 0.3701 | 0.8228   |
| 0.5744        | 22.0  | 550  | 0.8008          | 0.3062    | 0.5172 | 0.3847 | 0.8201   |
| 0.5744        | 23.0  | 575  | 0.7822          | 0.3008    | 0.5217 | 0.3816 | 0.8294   |
| 0.5744        | 24.0  | 600  | 0.8432          | 0.3148    | 0.5114 | 0.3897 | 0.8191   |
| 0.5744        | 25.0  | 625  | 0.8161          | 0.3387    | 0.5309 | 0.4135 | 0.8332   |
| 0.5744        | 26.0  | 650  | 0.8405          | 0.3289    | 0.5423 | 0.4095 | 0.8275   |
| 0.5744        | 27.0  | 675  | 0.8273          | 0.3465    | 0.5435 | 0.4232 | 0.8311   |
| 0.5744        | 28.0  | 700  | 0.8920          | 0.3326    | 0.5446 | 0.4130 | 0.8309   |
| 0.5744        | 29.0  | 725  | 0.8796          | 0.3303    | 0.5400 | 0.4099 | 0.8344   |
| 0.5744        | 30.0  | 750  | 0.8918          | 0.3319    | 0.5229 | 0.4060 | 0.8246   |
| 0.5744        | 31.0  | 775  | 0.8656          | 0.3613    | 0.5618 | 0.4398 | 0.8381   |
| 0.5744        | 32.0  | 800  | 0.9315          | 0.3375    | 0.5503 | 0.4184 | 0.8260   |
| 0.5744        | 33.0  | 825  | 0.9042          | 0.3644    | 0.5686 | 0.4441 | 0.8339   |
| 0.5744        | 34.0  | 850  | 0.9060          | 0.3865    | 0.5652 | 0.4591 | 0.8387   |
| 0.5744        | 35.0  | 875  | 0.9413          | 0.4021    | 0.5778 | 0.4742 | 0.8360   |
| 0.5744        | 36.0  | 900  | 0.9608          | 0.3634    | 0.5629 | 0.4417 | 0.8337   |
| 0.5744        | 37.0  | 925  | 0.8908          | 0.3536    | 0.5526 | 0.4313 | 0.8355   |
| 0.5744        | 38.0  | 950  | 0.9339          | 0.3543    | 0.5744 | 0.4382 | 0.8360   |
| 0.5744        | 39.0  | 975  | 0.9853          | 0.3751    | 0.5721 | 0.4531 | 0.8416   |
| 0.068         | 40.0  | 1000 | 0.9807          | 0.4005    | 0.5847 | 0.4753 | 0.8352   |
| 0.068         | 41.0  | 1025 | 1.0515          | 0.3953    | 0.5641 | 0.4649 | 0.8290   |
| 0.068         | 42.0  | 1050 | 0.9588          | 0.3912    | 0.5778 | 0.4665 | 0.8400   |
| 0.068         | 43.0  | 1075 | 0.9839          | 0.3888    | 0.5858 | 0.4674 | 0.8381   |
| 0.068         | 44.0  | 1100 | 1.0556          | 0.4092    | 0.5721 | 0.4771 | 0.8341   |
| 0.068         | 45.0  | 1125 | 0.9591          | 0.4097    | 0.5892 | 0.4833 | 0.8433   |
| 0.068         | 46.0  | 1150 | 1.0339          | 0.4057    | 0.5904 | 0.4809 | 0.8337   |
| 0.068         | 47.0  | 1175 | 1.0162          | 0.3871    | 0.5904 | 0.4676 | 0.8438   |
| 0.068         | 48.0  | 1200 | 1.0642          | 0.3864    | 0.5858 | 0.4657 | 0.8348   |
| 0.068         | 49.0  | 1225 | 1.0270          | 0.4257    | 0.5904 | 0.4947 | 0.8464   |
| 0.068         | 50.0  | 1250 | 1.0872          | 0.4126    | 0.6053 | 0.4907 | 0.8390   |
| 0.068         | 51.0  | 1275 | 1.0346          | 0.4086    | 0.5904 | 0.4829 | 0.8437   |
| 0.068         | 52.0  | 1300 | 1.0785          | 0.4131    | 0.6007 | 0.4895 | 0.8389   |
| 0.068         | 53.0  | 1325 | 1.0533          | 0.4380    | 0.5984 | 0.5058 | 0.8433   |
| 0.068         | 54.0  | 1350 | 1.0574          | 0.4109    | 0.5961 | 0.4865 | 0.8430   |
| 0.068         | 55.0  | 1375 | 1.1087          | 0.4166    | 0.5973 | 0.4908 | 0.8417   |
| 0.068         | 56.0  | 1400 | 1.0861          | 0.4140    | 0.5870 | 0.4856 | 0.8398   |
| 0.068         | 57.0  | 1425 | 1.0796          | 0.4085    | 0.6053 | 0.4878 | 0.8442   |
| 0.068         | 58.0  | 1450 | 1.1179          | 0.4208    | 0.6053 | 0.4965 | 0.8383   |
| 0.068         | 59.0  | 1475 | 1.1096          | 0.3950    | 0.5915 | 0.4737 | 0.8416   |
| 0.0173        | 60.0  | 1500 | 1.0741          | 0.4518    | 0.6167 | 0.5215 | 0.8440   |
| 0.0173        | 61.0  | 1525 | 1.0957          | 0.4536    | 0.6098 | 0.5203 | 0.8423   |
| 0.0173        | 62.0  | 1550 | 1.1131          | 0.4581    | 0.5881 | 0.5150 | 0.8455   |
| 0.0173        | 63.0  | 1575 | 1.0809          | 0.4367    | 0.6156 | 0.5109 | 0.8499   |
| 0.0173        | 64.0  | 1600 | 1.1138          | 0.4439    | 0.5927 | 0.5076 | 0.8419   |
| 0.0173        | 65.0  | 1625 | 1.1543          | 0.4100    | 0.5995 | 0.4870 | 0.8394   |
| 0.0173        | 66.0  | 1650 | 1.1292          | 0.4256    | 0.6087 | 0.5009 | 0.8432   |
| 0.0173        | 67.0  | 1675 | 1.1415          | 0.4542    | 0.6064 | 0.5194 | 0.8461   |
| 0.0173        | 68.0  | 1700 | 1.1804          | 0.4300    | 0.6007 | 0.5012 | 0.8436   |
| 0.0173        | 69.0  | 1725 | 1.1676          | 0.4356    | 0.5995 | 0.5046 | 0.8437   |
| 0.0173        | 70.0  | 1750 | 1.1806          | 0.4316    | 0.5961 | 0.5007 | 0.8420   |
| 0.0173        | 71.0  | 1775 | 1.1530          | 0.435     | 0.5973 | 0.5034 | 0.8459   |
| 0.0173        | 72.0  | 1800 | 1.1691          | 0.4344    | 0.5984 | 0.5034 | 0.8435   |
| 0.0173        | 73.0  | 1825 | 1.1869          | 0.4242    | 0.5927 | 0.4945 | 0.8410   |
| 0.0173        | 74.0  | 1850 | 1.1868          | 0.4450    | 0.5927 | 0.5083 | 0.8395   |
| 0.0173        | 75.0  | 1875 | 1.1987          | 0.4458    | 0.6064 | 0.5138 | 0.8398   |
| 0.0173        | 76.0  | 1900 | 1.1936          | 0.4396    | 0.5870 | 0.5027 | 0.8392   |
| 0.0173        | 77.0  | 1925 | 1.1882          | 0.4433    | 0.5950 | 0.5081 | 0.8414   |
| 0.0173        | 78.0  | 1950 | 1.2038          | 0.4387    | 0.5938 | 0.5046 | 0.8413   |
| 0.0173        | 79.0  | 1975 | 1.2103          | 0.4417    | 0.5984 | 0.5083 | 0.8403   |
| 0.0056        | 80.0  | 2000 | 1.2062          | 0.4259    | 0.5915 | 0.4952 | 0.8394   |
| 0.0056        | 81.0  | 2025 | 1.1871          | 0.4536    | 0.5984 | 0.5160 | 0.8425   |
| 0.0056        | 82.0  | 2050 | 1.1944          | 0.4268    | 0.6007 | 0.4990 | 0.8416   |
| 0.0056        | 83.0  | 2075 | 1.1941          | 0.4549    | 0.6007 | 0.5178 | 0.8447   |
| 0.0056        | 84.0  | 2100 | 1.2032          | 0.4553    | 0.6007 | 0.5180 | 0.8436   |
| 0.0056        | 85.0  | 2125 | 1.2096          | 0.4420    | 0.6018 | 0.5097 | 0.8414   |
| 0.0056        | 86.0  | 2150 | 1.2011          | 0.4333    | 0.6018 | 0.5038 | 0.8401   |
| 0.0056        | 87.0  | 2175 | 1.2329          | 0.4511    | 0.5961 | 0.5136 | 0.8411   |
| 0.0056        | 88.0  | 2200 | 1.2134          | 0.4523    | 0.6018 | 0.5164 | 0.8429   |
| 0.0056        | 89.0  | 2225 | 1.2281          | 0.4410    | 0.5984 | 0.5078 | 0.8426   |
| 0.0056        | 90.0  | 2250 | 1.2284          | 0.4490    | 0.6041 | 0.5151 | 0.8416   |
| 0.0056        | 91.0  | 2275 | 1.2129          | 0.435     | 0.5973 | 0.5034 | 0.8438   |
| 0.0056        | 92.0  | 2300 | 1.2164          | 0.4387    | 0.5973 | 0.5058 | 0.8428   |
| 0.0056        | 93.0  | 2325 | 1.2177          | 0.4429    | 0.6030 | 0.5107 | 0.8433   |
| 0.0056        | 94.0  | 2350 | 1.2297          | 0.4545    | 0.6053 | 0.5191 | 0.8434   |
| 0.0056        | 95.0  | 2375 | 1.2243          | 0.4579    | 0.6030 | 0.5205 | 0.8459   |
| 0.0056        | 96.0  | 2400 | 1.2241          | 0.4478    | 0.6041 | 0.5144 | 0.8457   |
| 0.0056        | 97.0  | 2425 | 1.2286          | 0.4496    | 0.6018 | 0.5147 | 0.8434   |
| 0.0056        | 98.0  | 2450 | 1.2279          | 0.4426    | 0.5995 | 0.5092 | 0.8431   |
| 0.0056        | 99.0  | 2475 | 1.2244          | 0.4328    | 0.6007 | 0.5031 | 0.8443   |
| 0.0029        | 100.0 | 2500 | 1.2239          | 0.4333    | 0.5984 | 0.5026 | 0.8446   |


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


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


* Loss: 1.2239
* Precision: 0.4333
* Recall: 0.5984
* F1: 0.5026
* Accuracy: 0.8446


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