metadata

library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
  - generated_from_trainer
datasets:
  - conllpp
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-multilingual-cased-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conllpp
          type: conllpp
          config: conllpp
          split: validation
          args: conllpp
        metrics:
          - name: Precision
            type: precision
            value: 0.9282027217268888
          - name: Recall
            type: recall
            value: 0.9339881008593823
          - name: F1
            type: f1
            value: 0.9310864244021841
          - name: Accuracy
            type: accuracy
            value: 0.9838898310040348

distilbert-base-multilingual-cased-finetuned-ner

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

Loss: 0.0632
Precision: 0.9282
Recall: 0.9340
F1: 0.9311
Accuracy: 0.9839

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.237	1.0	878	0.0732	0.9083	0.9188	0.9135	0.9794
0.0533	2.0	1756	0.0648	0.9265	0.9274	0.9269	0.9827
0.0303	3.0	2634	0.0632	0.9282	0.9340	0.9311	0.9839

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3