---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-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: 0.8763335204941044
    - name: Recall
      type: recall
      value: 0.9115199299167762
    - name: F1
      type: f1
      value: 0.8935804766335075
    - name: Accuracy
      type: accuracy
      value: 0.9956876979901592
---

<!-- 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. -->

# distilbert-base-multilingual-cased-mapa_fine-ner

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0282
- Precision: 0.8763
- Recall: 0.9115
- F1: 0.8936
- Accuracy: 0.9957

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0244        | 1.0   | 1739  | 0.0202          | 0.8083    | 0.9314 | 0.8655 | 0.9941   |
| 0.0154        | 2.0   | 3478  | 0.0173          | 0.8813    | 0.9006 | 0.8908 | 0.9954   |
| 0.0118        | 3.0   | 5217  | 0.0161          | 0.8885    | 0.9131 | 0.9006 | 0.9960   |
| 0.0084        | 4.0   | 6956  | 0.0194          | 0.8485    | 0.9295 | 0.8871 | 0.9953   |
| 0.0069        | 5.0   | 8695  | 0.0219          | 0.8583    | 0.9198 | 0.8880 | 0.9953   |
| 0.0054        | 6.0   | 10434 | 0.0229          | 0.8622    | 0.9160 | 0.8883 | 0.9954   |
| 0.0032        | 7.0   | 12173 | 0.0248          | 0.8817    | 0.8979 | 0.8898 | 0.9956   |
| 0.0023        | 8.0   | 13912 | 0.0265          | 0.8900    | 0.9023 | 0.8961 | 0.9958   |
| 0.0018        | 9.0   | 15651 | 0.0275          | 0.8657    | 0.9137 | 0.8890 | 0.9954   |
| 0.0016        | 10.0  | 17390 | 0.0282          | 0.8763    | 0.9115 | 0.8936 | 0.9957   |


### Framework versions

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