---
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
---

<!-- 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-finetuned-ner

This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/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