---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: encoder
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.922421290659245
    - name: Recall
      type: recall
      value: 0.9389164305949008
    - name: F1
      type: f1
      value: 0.9305957708168815
    - name: Accuracy
      type: accuracy
      value: 0.9842790998169484
---

<!-- 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. -->

# encoder

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2344
- Precision: 0.9224
- Recall: 0.9389
- F1: 0.9306
- Accuracy: 0.9843

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.657         | 0.3333 | 1441  | 0.1806          | 0.8261    | 0.8383 | 0.8322 | 0.9700   |
| 0.0884        | 0.6667 | 2882  | 0.1383          | 0.8822    | 0.8913 | 0.8867 | 0.9783   |
| 0.0637        | 1.0    | 4323  | 0.1343          | 0.9032    | 0.9132 | 0.9082 | 0.9811   |
| 0.0427        | 1.3333 | 5764  | 0.1527          | 0.9014    | 0.9210 | 0.9111 | 0.9817   |
| 0.0412        | 1.6667 | 7205  | 0.1450          | 0.9109    | 0.9301 | 0.9204 | 0.9838   |
| 0.0391        | 2.0    | 8646  | 0.1459          | 0.9145    | 0.9221 | 0.9183 | 0.9832   |
| 0.0235        | 2.3333 | 10087 | 0.1848          | 0.9041    | 0.9313 | 0.9175 | 0.9821   |
| 0.0228        | 2.6667 | 11528 | 0.1539          | 0.9188    | 0.9375 | 0.9281 | 0.9846   |
| 0.0283        | 3.0    | 12969 | 0.1513          | 0.9137    | 0.9295 | 0.9215 | 0.9833   |
| 0.0176        | 3.3333 | 14410 | 0.1748          | 0.9232    | 0.9347 | 0.9289 | 0.9842   |
| 0.0177        | 3.6667 | 15851 | 0.1706          | 0.9234    | 0.9331 | 0.9282 | 0.9848   |
| 0.0191        | 4.0    | 17292 | 0.1784          | 0.9095    | 0.9309 | 0.9201 | 0.9829   |
| 0.0131        | 4.3333 | 18733 | 0.1862          | 0.9130    | 0.9361 | 0.9244 | 0.9833   |
| 0.0138        | 4.6667 | 20174 | 0.1883          | 0.9133    | 0.9322 | 0.9226 | 0.9827   |
| 0.0128        | 5.0    | 21615 | 0.1986          | 0.9104    | 0.9304 | 0.9203 | 0.9820   |
| 0.0112        | 5.3333 | 23056 | 0.2002          | 0.9172    | 0.9356 | 0.9263 | 0.9833   |
| 0.0097        | 5.6667 | 24497 | 0.1784          | 0.9257    | 0.9394 | 0.9325 | 0.9846   |
| 0.0068        | 6.0    | 25938 | 0.1929          | 0.9210    | 0.9333 | 0.9271 | 0.9838   |
| 0.0068        | 6.3333 | 27379 | 0.2086          | 0.9212    | 0.9382 | 0.9296 | 0.9840   |
| 0.0057        | 6.6667 | 28820 | 0.2035          | 0.9240    | 0.9368 | 0.9304 | 0.9844   |
| 0.006         | 7.0    | 30261 | 0.2098          | 0.9198    | 0.9379 | 0.9287 | 0.9841   |
| 0.0042        | 7.3333 | 31702 | 0.2236          | 0.9182    | 0.9327 | 0.9254 | 0.9835   |
| 0.0054        | 7.6667 | 33143 | 0.2267          | 0.9196    | 0.9361 | 0.9278 | 0.9833   |
| 0.0029        | 8.0    | 34584 | 0.2162          | 0.9257    | 0.9375 | 0.9316 | 0.9846   |
| 0.0022        | 8.3333 | 36025 | 0.2120          | 0.9241    | 0.9403 | 0.9322 | 0.9849   |
| 0.0045        | 8.6667 | 37466 | 0.2185          | 0.9247    | 0.9393 | 0.9319 | 0.9846   |
| 0.0029        | 9.0    | 38907 | 0.2182          | 0.9247    | 0.9387 | 0.9316 | 0.9846   |
| 0.0021        | 9.3333 | 40348 | 0.2316          | 0.9231    | 0.9394 | 0.9312 | 0.9842   |
| 0.002         | 9.6667 | 41789 | 0.2358          | 0.9226    | 0.9387 | 0.9306 | 0.9842   |
| 0.0019        | 10.0   | 43230 | 0.2344          | 0.9224    | 0.9389 | 0.9306 | 0.9843   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1