---
license: apache-2.0
tags:
- generated_from_trainer
- name-entity-recognition
- legal
datasets:
- lextreme
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-multilingual-cased-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: 0.7191116088092572
    - name: Recall
      type: recall
      value: 0.6452855468095796
    - name: F1
      type: f1
      value: 0.6802012534204254
    - name: Accuracy
      type: accuracy
      value: 0.9878756336348935
language:
- en
- fr
- it
- es
- de
- nl
- pl
- ru
- pt
---

<!-- 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_coarse-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: 0.0882
- Precision: 0.7191
- Recall: 0.6453
- F1: 0.6802
- Accuracy: 0.9879

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0248        | 1.0   | 1739  | 0.0528          | 0.7451    | 0.5805 | 0.6525 | 0.9871   |
| 0.0181        | 2.0   | 3478  | 0.0595          | 0.7369    | 0.5749 | 0.6459 | 0.9875   |
| 0.0121        | 3.0   | 5217  | 0.0499          | 0.7404    | 0.6280 | 0.6796 | 0.9879   |
| 0.0088        | 4.0   | 6956  | 0.0634          | 0.6912    | 0.6334 | 0.6610 | 0.9875   |
| 0.0072        | 5.0   | 8695  | 0.0625          | 0.7109    | 0.6478 | 0.6779 | 0.9880   |
| 0.0052        | 6.0   | 10434 | 0.0702          | 0.7098    | 0.6518 | 0.6796 | 0.9878   |
| 0.0041        | 7.0   | 12173 | 0.0733          | 0.7176    | 0.6429 | 0.6782 | 0.9878   |
| 0.0026        | 8.0   | 13912 | 0.0779          | 0.7198    | 0.6540 | 0.6853 | 0.9879   |
| 0.0019        | 9.0   | 15651 | 0.0875          | 0.7181    | 0.6419 | 0.6779 | 0.9877   |
| 0.0018        | 10.0  | 17390 | 0.0882          | 0.7191    | 0.6453 | 0.6802 | 0.9879   |


### Framework versions

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