pytorch roberta token-classification dataset:conll2003 transformers generated_from_trainer model-index autotrain_compatible
---
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
---

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# 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
