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# xlm-roberta-base-finetuned-conll2003

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0898
- F1: 0.9484

## 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: 5e-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 | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1415        | 1.0   | 439   | 0.0447          | 0.9367 |
| 0.0429        | 2.0   | 878   | 0.0437          | 0.9310 |
| 0.0259        | 3.0   | 1317  | 0.0534          | 0.9328 |
| 0.0195        | 4.0   | 1756  | 0.0449          | 0.9429 |
| 0.0146        | 5.0   | 2195  | 0.0484          | 0.9421 |
| 0.0121        | 6.0   | 2634  | 0.0523          | 0.9392 |
| 0.0099        | 7.0   | 3073  | 0.0500          | 0.9428 |
| 0.0077        | 8.0   | 3512  | 0.0536          | 0.9423 |
| 0.008         | 9.0   | 3951  | 0.0672          | 0.9254 |
| 0.0079        | 10.0  | 4390  | 0.0589          | 0.9442 |
| 0.007         | 11.0  | 4829  | 0.0669          | 0.9400 |
| 0.0051        | 12.0  | 5268  | 0.0602          | 0.9409 |
| 0.0052        | 13.0  | 5707  | 0.0639          | 0.9441 |
| 0.0036        | 14.0  | 6146  | 0.0635          | 0.9431 |
| 0.0033        | 15.0  | 6585  | 0.0858          | 0.9328 |
| 0.0038        | 16.0  | 7024  | 0.0653          | 0.9478 |
| 0.0047        | 17.0  | 7463  | 0.0689          | 0.9431 |
| 0.0039        | 18.0  | 7902  | 0.0687          | 0.9442 |
| 0.0031        | 19.0  | 8341  | 0.0687          | 0.9459 |
| 0.0027        | 20.0  | 8780  | 0.0785          | 0.9424 |
| 0.0047        | 21.0  | 9219  | 0.0654          | 0.9444 |
| 0.0035        | 22.0  | 9658  | 0.0748          | 0.9454 |
| 0.0021        | 23.0  | 10097 | 0.0714          | 0.9423 |
| 0.003         | 24.0  | 10536 | 0.0730          | 0.9433 |
| 0.0031        | 25.0  | 10975 | 0.0682          | 0.9417 |
| 0.0021        | 26.0  | 11414 | 0.0762          | 0.9407 |
| 0.0025        | 27.0  | 11853 | 0.0773          | 0.9391 |
| 0.0019        | 28.0  | 12292 | 0.0739          | 0.9420 |
| 0.0032        | 29.0  | 12731 | 0.0755          | 0.9413 |
| 0.0023        | 30.0  | 13170 | 0.0755          | 0.9439 |
| 0.0024        | 31.0  | 13609 | 0.0747          | 0.9456 |
| 0.0018        | 32.0  | 14048 | 0.0730          | 0.9430 |
| 0.0017        | 33.0  | 14487 | 0.0866          | 0.9385 |
| 0.0019        | 34.0  | 14926 | 0.0695          | 0.9440 |
| 0.0016        | 35.0  | 15365 | 0.0818          | 0.9442 |
| 0.0034        | 36.0  | 15804 | 0.0750          | 0.9459 |
| 0.0019        | 37.0  | 16243 | 0.0808          | 0.9414 |
| 0.0013        | 38.0  | 16682 | 0.0797          | 0.9422 |
| 0.0015        | 39.0  | 17121 | 0.0814          | 0.9394 |
| 0.0019        | 40.0  | 17560 | 0.0757          | 0.9415 |
| 0.0011        | 41.0  | 17999 | 0.0778          | 0.9453 |
| 0.0011        | 42.0  | 18438 | 0.0825          | 0.9407 |
| 0.0012        | 43.0  | 18877 | 0.0767          | 0.9458 |
| 0.0022        | 44.0  | 19316 | 0.0865          | 0.9396 |
| 0.0009        | 45.0  | 19755 | 0.0826          | 0.9459 |
| 0.0008        | 46.0  | 20194 | 0.0819          | 0.9473 |
| 0.0017        | 47.0  | 20633 | 0.0844          | 0.9420 |
| 0.0015        | 48.0  | 21072 | 0.0827          | 0.9448 |
| 0.0014        | 49.0  | 21511 | 0.0800          | 0.9464 |
| 0.0008        | 50.0  | 21950 | 0.0770          | 0.9474 |
| 0.0011        | 51.0  | 22389 | 0.0766          | 0.9471 |
| 0.0006        | 52.0  | 22828 | 0.0896          | 0.9424 |
| 0.0011        | 53.0  | 23267 | 0.0866          | 0.9425 |
| 0.001         | 54.0  | 23706 | 0.0853          | 0.9426 |
| 0.0007        | 55.0  | 24145 | 0.0831          | 0.9462 |
| 0.0008        | 56.0  | 24584 | 0.0805          | 0.9457 |
| 0.0008        | 57.0  | 25023 | 0.0866          | 0.9438 |
| 0.0008        | 58.0  | 25462 | 0.0822          | 0.9421 |
| 0.0011        | 59.0  | 25901 | 0.0837          | 0.9417 |
| 0.0007        | 60.0  | 26340 | 0.0823          | 0.9466 |
| 0.0008        | 61.0  | 26779 | 0.0825          | 0.9425 |
| 0.0004        | 62.0  | 27218 | 0.0825          | 0.9433 |
| 0.0005        | 63.0  | 27657 | 0.0826          | 0.9435 |
| 0.0004        | 64.0  | 28096 | 0.0838          | 0.9437 |
| 0.0008        | 65.0  | 28535 | 0.0909          | 0.9424 |
| 0.0004        | 66.0  | 28974 | 0.0825          | 0.9464 |
| 0.0004        | 67.0  | 29413 | 0.0917          | 0.9454 |
| 0.0004        | 68.0  | 29852 | 0.0843          | 0.9487 |
| 0.0005        | 69.0  | 30291 | 0.0825          | 0.9481 |
| 0.0003        | 70.0  | 30730 | 0.0825          | 0.9456 |
| 0.0005        | 71.0  | 31169 | 0.0835          | 0.9460 |
| 0.0003        | 72.0  | 31608 | 0.0906          | 0.9481 |
| 0.0001        | 73.0  | 32047 | 0.0916          | 0.9471 |
| 0.0007        | 74.0  | 32486 | 0.0885          | 0.9460 |
| 0.0003        | 75.0  | 32925 | 0.0879          | 0.9481 |
| 0.0001        | 76.0  | 33364 | 0.0871          | 0.9505 |
| 0.0002        | 77.0  | 33803 | 0.0906          | 0.9486 |
| 0.0003        | 78.0  | 34242 | 0.0934          | 0.9469 |
| 0.0002        | 79.0  | 34681 | 0.0911          | 0.9466 |
| 0.0003        | 80.0  | 35120 | 0.0871          | 0.9489 |
| 0.0003        | 81.0  | 35559 | 0.0876          | 0.9494 |
| 0.0002        | 82.0  | 35998 | 0.0884          | 0.9482 |
| 0.0001        | 83.0  | 36437 | 0.0910          | 0.9469 |
| 0.0002        | 84.0  | 36876 | 0.0874          | 0.9473 |
| 0.0002        | 85.0  | 37315 | 0.0864          | 0.9463 |
| 0.0001        | 86.0  | 37754 | 0.0878          | 0.9472 |
| 0.0002        | 87.0  | 38193 | 0.0836          | 0.9500 |
| 0.0001        | 88.0  | 38632 | 0.0861          | 0.9495 |
| 0.0001        | 89.0  | 39071 | 0.0869          | 0.9503 |
| 0.0001        | 90.0  | 39510 | 0.0878          | 0.9480 |
| 0.0001        | 91.0  | 39949 | 0.0878          | 0.9501 |
| 0.0           | 92.0  | 40388 | 0.0886          | 0.9477 |
| 0.0001        | 93.0  | 40827 | 0.0884          | 0.9497 |
| 0.0001        | 94.0  | 41266 | 0.0897          | 0.9487 |
| 0.0001        | 95.0  | 41705 | 0.0896          | 0.9490 |
| 0.0001        | 96.0  | 42144 | 0.0879          | 0.9499 |
| 0.0001        | 97.0  | 42583 | 0.0884          | 0.9490 |
| 0.0001        | 98.0  | 43022 | 0.0899          | 0.9486 |
| 0.0001        | 99.0  | 43461 | 0.0897          | 0.9488 |
| 0.0001        | 100.0 | 43900 | 0.0898          | 0.9484 |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-conll2003", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "config": "conll2003", "split": "validation", "args": "conll2003"}, "metrics": [{"type": "f1", "value": 0.948444966049124, "name": "F1"}]}]}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer dataset:conll2003 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

xlm-roberta-base-finetuned-conll2003
====================================


This model is a fine-tuned version of xlm-roberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0898
* F1: 0.9484


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: 5e-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 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3