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---
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: twitter-roberta-base-CoNLL
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.953111963957951
- name: Recall
type: recall
value: 0.9612924941097274
- name: F1
type: f1
value: 0.9571847507331379
- name: Accuracy
type: accuracy
value: 0.9925820645613489
---
<!-- 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. -->
# twitter-roberta-base-CoNLL
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0423
- Precision: 0.9531
- Recall: 0.9613
- F1: 0.9572
- Accuracy: 0.9926
## 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: 6e-05
- train_batch_size: 64
- eval_batch_size: 1024
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.11 | 25 | 0.2063 | 0.6517 | 0.6659 | 0.6587 | 0.9386 |
| No log | 0.23 | 50 | 0.0810 | 0.8373 | 0.8766 | 0.8565 | 0.9771 |
| No log | 0.34 | 75 | 0.0651 | 0.8937 | 0.9058 | 0.8997 | 0.9827 |
| No log | 0.45 | 100 | 0.0537 | 0.9014 | 0.9135 | 0.9074 | 0.9849 |
| No log | 0.57 | 125 | 0.0464 | 0.9097 | 0.9244 | 0.9170 | 0.9867 |
| No log | 0.68 | 150 | 0.0423 | 0.9243 | 0.9350 | 0.9296 | 0.9885 |
| No log | 0.8 | 175 | 0.0381 | 0.9250 | 0.9438 | 0.9343 | 0.9900 |
| No log | 0.91 | 200 | 0.0388 | 0.9264 | 0.9446 | 0.9354 | 0.9896 |
| No log | 1.02 | 225 | 0.0394 | 0.9328 | 0.9441 | 0.9384 | 0.9898 |
| No log | 1.14 | 250 | 0.0423 | 0.9348 | 0.9458 | 0.9403 | 0.9896 |
| No log | 1.25 | 275 | 0.0432 | 0.9304 | 0.9406 | 0.9355 | 0.9892 |
| No log | 1.36 | 300 | 0.0382 | 0.9393 | 0.9473 | 0.9433 | 0.9901 |
| No log | 1.48 | 325 | 0.0381 | 0.9326 | 0.9504 | 0.9414 | 0.9901 |
| No log | 1.59 | 350 | 0.0387 | 0.9337 | 0.9524 | 0.9429 | 0.9902 |
| No log | 1.7 | 375 | 0.0365 | 0.9404 | 0.9475 | 0.9439 | 0.9901 |
| No log | 1.82 | 400 | 0.0382 | 0.9431 | 0.9517 | 0.9474 | 0.9905 |
| No log | 1.93 | 425 | 0.0373 | 0.9399 | 0.9524 | 0.9461 | 0.9903 |
| No log | 2.05 | 450 | 0.0367 | 0.9440 | 0.9556 | 0.9497 | 0.9910 |
| No log | 2.16 | 475 | 0.0396 | 0.9400 | 0.9551 | 0.9475 | 0.9907 |
| 0.0771 | 2.27 | 500 | 0.0353 | 0.9442 | 0.9574 | 0.9508 | 0.9912 |
| 0.0771 | 2.39 | 525 | 0.0394 | 0.9401 | 0.9507 | 0.9454 | 0.9906 |
| 0.0771 | 2.5 | 550 | 0.0370 | 0.9447 | 0.9522 | 0.9485 | 0.9910 |
| 0.0771 | 2.61 | 575 | 0.0352 | 0.9404 | 0.9541 | 0.9472 | 0.9908 |
| 0.0771 | 2.73 | 600 | 0.0386 | 0.9345 | 0.9554 | 0.9448 | 0.9908 |
| 0.0771 | 2.84 | 625 | 0.0366 | 0.9428 | 0.9576 | 0.9502 | 0.9916 |
| 0.0771 | 2.95 | 650 | 0.0353 | 0.9427 | 0.9546 | 0.9486 | 0.9913 |
| 0.0771 | 3.07 | 675 | 0.0359 | 0.9412 | 0.9544 | 0.9478 | 0.9911 |
| 0.0771 | 3.18 | 700 | 0.0356 | 0.9476 | 0.9593 | 0.9534 | 0.9920 |
| 0.0771 | 3.3 | 725 | 0.0345 | 0.9484 | 0.9586 | 0.9535 | 0.9918 |
| 0.0771 | 3.41 | 750 | 0.0345 | 0.9427 | 0.9557 | 0.9492 | 0.9916 |
| 0.0771 | 3.52 | 775 | 0.0364 | 0.9389 | 0.9569 | 0.9478 | 0.9914 |
| 0.0771 | 3.64 | 800 | 0.0360 | 0.9430 | 0.9584 | 0.9507 | 0.9915 |
| 0.0771 | 3.75 | 825 | 0.0387 | 0.9458 | 0.9552 | 0.9505 | 0.9915 |
| 0.0771 | 3.86 | 850 | 0.0347 | 0.9468 | 0.9576 | 0.9521 | 0.9917 |
| 0.0771 | 3.98 | 875 | 0.0357 | 0.9445 | 0.9574 | 0.9509 | 0.9915 |
| 0.0771 | 4.09 | 900 | 0.0382 | 0.9464 | 0.9578 | 0.9521 | 0.9918 |
| 0.0771 | 4.2 | 925 | 0.0391 | 0.9475 | 0.9562 | 0.9518 | 0.9918 |
| 0.0771 | 4.32 | 950 | 0.0428 | 0.9466 | 0.9547 | 0.9506 | 0.9912 |
| 0.0771 | 4.43 | 975 | 0.0404 | 0.9459 | 0.9554 | 0.9506 | 0.9913 |
| 0.0118 | 4.55 | 1000 | 0.0403 | 0.9375 | 0.9549 | 0.9461 | 0.9909 |
| 0.0118 | 4.66 | 1025 | 0.0369 | 0.9482 | 0.9586 | 0.9534 | 0.9919 |
| 0.0118 | 4.77 | 1050 | 0.0374 | 0.9457 | 0.9584 | 0.9520 | 0.9918 |
| 0.0118 | 4.89 | 1075 | 0.0359 | 0.9507 | 0.9571 | 0.9539 | 0.9923 |
| 0.0118 | 5.0 | 1100 | 0.0373 | 0.9453 | 0.9594 | 0.9523 | 0.9919 |
| 0.0118 | 5.11 | 1125 | 0.0370 | 0.9499 | 0.9594 | 0.9546 | 0.9924 |
| 0.0118 | 5.23 | 1150 | 0.0388 | 0.9510 | 0.9601 | 0.9555 | 0.9922 |
| 0.0118 | 5.34 | 1175 | 0.0395 | 0.9486 | 0.9559 | 0.9522 | 0.9920 |
| 0.0118 | 5.45 | 1200 | 0.0391 | 0.9495 | 0.9591 | 0.9543 | 0.9924 |
| 0.0118 | 5.57 | 1225 | 0.0378 | 0.9517 | 0.9588 | 0.9552 | 0.9923 |
| 0.0118 | 5.68 | 1250 | 0.0388 | 0.9515 | 0.9615 | 0.9565 | 0.9924 |
| 0.0118 | 5.8 | 1275 | 0.0384 | 0.9512 | 0.9610 | 0.9560 | 0.9924 |
| 0.0118 | 5.91 | 1300 | 0.0395 | 0.9530 | 0.9613 | 0.9571 | 0.9924 |
| 0.0118 | 6.02 | 1325 | 0.0408 | 0.9499 | 0.9569 | 0.9534 | 0.9919 |
| 0.0118 | 6.14 | 1350 | 0.0412 | 0.9481 | 0.9616 | 0.9548 | 0.9922 |
| 0.0118 | 6.25 | 1375 | 0.0413 | 0.9521 | 0.9591 | 0.9556 | 0.9924 |
| 0.0118 | 6.36 | 1400 | 0.0412 | 0.9466 | 0.9584 | 0.9525 | 0.9917 |
| 0.0118 | 6.48 | 1425 | 0.0405 | 0.9504 | 0.9608 | 0.9556 | 0.9921 |
| 0.0118 | 6.59 | 1450 | 0.0400 | 0.9517 | 0.9615 | 0.9566 | 0.9925 |
| 0.0118 | 6.7 | 1475 | 0.0398 | 0.9510 | 0.9594 | 0.9552 | 0.9923 |
| 0.0049 | 6.82 | 1500 | 0.0395 | 0.9523 | 0.9615 | 0.9569 | 0.9925 |
| 0.0049 | 6.93 | 1525 | 0.0392 | 0.9520 | 0.9623 | 0.9571 | 0.9927 |
| 0.0049 | 7.05 | 1550 | 0.0390 | 0.9511 | 0.9593 | 0.9552 | 0.9923 |
| 0.0049 | 7.16 | 1575 | 0.0393 | 0.9520 | 0.9611 | 0.9565 | 0.9925 |
| 0.0049 | 7.27 | 1600 | 0.0389 | 0.9512 | 0.9613 | 0.9562 | 0.9925 |
| 0.0049 | 7.39 | 1625 | 0.0405 | 0.9518 | 0.9613 | 0.9565 | 0.9924 |
| 0.0049 | 7.5 | 1650 | 0.0410 | 0.9512 | 0.9606 | 0.9559 | 0.9925 |
| 0.0049 | 7.61 | 1675 | 0.0408 | 0.9526 | 0.9613 | 0.9569 | 0.9925 |
| 0.0049 | 7.73 | 1700 | 0.0436 | 0.9482 | 0.9610 | 0.9545 | 0.9922 |
| 0.0049 | 7.84 | 1725 | 0.0419 | 0.9495 | 0.9625 | 0.9560 | 0.9924 |
| 0.0049 | 7.95 | 1750 | 0.0429 | 0.9525 | 0.9618 | 0.9571 | 0.9926 |
| 0.0049 | 8.07 | 1775 | 0.0419 | 0.9509 | 0.9615 | 0.9562 | 0.9924 |
| 0.0049 | 8.18 | 1800 | 0.0422 | 0.9510 | 0.9601 | 0.9555 | 0.9923 |
| 0.0049 | 8.3 | 1825 | 0.0417 | 0.9521 | 0.9603 | 0.9562 | 0.9924 |
| 0.0049 | 8.41 | 1850 | 0.0415 | 0.9529 | 0.9611 | 0.9570 | 0.9925 |
| 0.0049 | 8.52 | 1875 | 0.0416 | 0.9523 | 0.9611 | 0.9567 | 0.9924 |
| 0.0049 | 8.64 | 1900 | 0.0419 | 0.9504 | 0.9608 | 0.9556 | 0.9922 |
| 0.0049 | 8.75 | 1925 | 0.0417 | 0.9520 | 0.9610 | 0.9564 | 0.9924 |
| 0.0049 | 8.86 | 1950 | 0.0419 | 0.9535 | 0.9621 | 0.9578 | 0.9926 |
| 0.0049 | 8.98 | 1975 | 0.0422 | 0.9531 | 0.9620 | 0.9575 | 0.9927 |
| 0.0022 | 9.09 | 2000 | 0.0423 | 0.9531 | 0.9613 | 0.9572 | 0.9926 |
| 0.0022 | 9.2 | 2025 | 0.0426 | 0.9520 | 0.9615 | 0.9567 | 0.9925 |
| 0.0022 | 9.32 | 2050 | 0.0425 | 0.9515 | 0.9606 | 0.9560 | 0.9925 |
| 0.0022 | 9.43 | 2075 | 0.0422 | 0.9517 | 0.9613 | 0.9565 | 0.9925 |
| 0.0022 | 9.55 | 2100 | 0.0423 | 0.9513 | 0.9606 | 0.9560 | 0.9925 |
| 0.0022 | 9.66 | 2125 | 0.0424 | 0.9513 | 0.9605 | 0.9559 | 0.9925 |
| 0.0022 | 9.77 | 2150 | 0.0423 | 0.9522 | 0.9611 | 0.9566 | 0.9925 |
| 0.0022 | 9.89 | 2175 | 0.0423 | 0.9522 | 0.9613 | 0.9567 | 0.9925 |
| 0.0022 | 10.0 | 2200 | 0.0422 | 0.9525 | 0.9616 | 0.9570 | 0.9925 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|