---
license: mit
tags:
- generated_from_trainer
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-mapa_fine-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lextreme
      type: lextreme
      config: mapa_fine
      split: test
      args: mapa_fine
    metrics:
    - name: Precision
      type: precision
      value: 0.7395134779750164
    - name: Recall
      type: recall
      value: 0.8236672524897481
    - name: F1
      type: f1
      value: 0.7793251576248873
    - name: Accuracy
      type: accuracy
      value: 0.991740752278482
---

<!-- 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. -->

# roberta-base-mapa_fine-ner

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0401
- Precision: 0.7395
- Recall: 0.8237
- F1: 0.7793
- Accuracy: 0.9917

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0877        | 1.0   | 1739  | 0.0495          | 0.6861    | 0.7595 | 0.7209 | 0.9903   |
| 0.0661        | 2.0   | 3478  | 0.0432          | 0.7278    | 0.8092 | 0.7663 | 0.9914   |
| 0.0633        | 3.0   | 5217  | 0.0403          | 0.7469    | 0.8128 | 0.7785 | 0.9919   |
| 0.059         | 4.0   | 6956  | 0.0401          | 0.7412    | 0.8196 | 0.7784 | 0.9918   |
| 0.063         | 5.0   | 8695  | 0.0400          | 0.7425    | 0.8200 | 0.7793 | 0.9918   |
| 0.0593        | 6.0   | 10434 | 0.0405          | 0.7332    | 0.8244 | 0.7761 | 0.9916   |
| 0.0595        | 7.0   | 12173 | 0.0400          | 0.7389    | 0.8222 | 0.7783 | 0.9917   |
| 0.0593        | 8.0   | 13912 | 0.0401          | 0.7390    | 0.8229 | 0.7787 | 0.9917   |
| 0.0594        | 9.0   | 15651 | 0.0402          | 0.7374    | 0.8240 | 0.7783 | 0.9917   |
| 0.0597        | 10.0  | 17390 | 0.0401          | 0.7395    | 0.8237 | 0.7793 | 0.9917   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2