metadata

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

distilbert-base-multilingual-cased-mapa_coarse-ner

This model is a fine-tuned version of 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