---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-mapa_coarse-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lextreme
      type: lextreme
      config: mapa_coarse
      split: test
      args: mapa_coarse
    metrics:
    - name: Precision
      type: precision
      value: 0.7440758293838863
    - name: Recall
      type: recall
      value: 0.5805042016806723
    - name: F1
      type: f1
      value: 0.652190332326284
    - name: Accuracy
      type: accuracy
      value: 0.9871584939520047
---

<!-- 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. -->

# distilroberta-base-mapa_coarse-ner

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1020
- Precision: 0.7441
- Recall: 0.5805
- F1: 0.6522
- Accuracy: 0.9872

## 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0343        | 1.0   | 1739  | 0.0694          | 0.6342    | 0.5205 | 0.5718 | 0.9841   |
| 0.0263        | 2.0   | 3478  | 0.0705          | 0.7961    | 0.5235 | 0.6317 | 0.9865   |
| 0.0183        | 3.0   | 5217  | 0.0670          | 0.7417    | 0.5313 | 0.6191 | 0.9864   |
| 0.015         | 4.0   | 6956  | 0.0632          | 0.7237    | 0.5850 | 0.6470 | 0.9869   |
| 0.0137        | 5.0   | 8695  | 0.0663          | 0.7311    | 0.6064 | 0.6629 | 0.9872   |
| 0.011         | 6.0   | 10434 | 0.0703          | 0.7163    | 0.5877 | 0.6457 | 0.9868   |
| 0.0096        | 7.0   | 12173 | 0.0799          | 0.7511    | 0.5676 | 0.6466 | 0.9871   |
| 0.0071        | 8.0   | 13912 | 0.0770          | 0.7386    | 0.5640 | 0.6396 | 0.9868   |
| 0.0068        | 9.0   | 15651 | 0.0827          | 0.7285    | 0.5674 | 0.6379 | 0.9868   |
| 0.0057        | 10.0  | 17390 | 0.0897          | 0.7611    | 0.5719 | 0.6531 | 0.9872   |
| 0.0053        | 11.0  | 19129 | 0.0940          | 0.7614    | 0.5627 | 0.6471 | 0.9871   |
| 0.004         | 12.0  | 20868 | 0.0874          | 0.7184    | 0.6084 | 0.6588 | 0.9873   |
| 0.0035        | 13.0  | 22607 | 0.0986          | 0.7513    | 0.5766 | 0.6525 | 0.9872   |
| 0.003         | 14.0  | 24346 | 0.1012          | 0.7396    | 0.5805 | 0.6505 | 0.9871   |
| 0.0026        | 15.0  | 26085 | 0.1020          | 0.7441    | 0.5805 | 0.6522 | 0.9872   |


### Framework versions

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