README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: mbert-fine-tune-ner_fr
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          config: fr
          split: validation
          args: fr
        metrics:
          - name: Precision
            type: precision
            value: 0.8999553969669938
          - name: Recall
            type: recall
            value: 0.9077698294866604
          - name: F1
            type: f1
            value: 0.9038457231168949
          - name: Accuracy
            type: accuracy
            value: 0.9491301830743971

mbert-fine-tune-ner_fr

This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset. It achieves the following results on the evaluation set:

• Loss: 0.1887
• Precision: 0.9000
• Recall: 0.9078
• F1: 0.9038
• Accuracy: 0.9491

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: 5e-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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2929	1.0	1250	0.2028	0.8782	0.8938	0.8860	0.9417
0.1355	2.0	2500	0.1887	0.9000	0.9078	0.9038	0.9491

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2