---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v4.012
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: caner
      type: caner
      config: default
      split: train[67%:68%]
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.7985739750445633
    - name: Recall
      type: recall
      value: 0.8373831775700935
    - name: F1
      type: f1
      value: 0.8175182481751825
    - name: Accuracy
      type: accuracy
      value: 0.9538148524923703
---

<!-- 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. -->

# bert-finetuned-ner-v4.012

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2304
- Precision: 0.7986
- Recall: 0.8374
- F1: 0.8175
- Accuracy: 0.9538

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2776        | 1.0   | 3228 | 0.3328          | 0.7988    | 0.7720 | 0.7852 | 0.9406   |
| 0.1617        | 2.0   | 6456 | 0.2514          | 0.8240    | 0.8224 | 0.8232 | 0.9591   |
| 0.1266        | 3.0   | 9684 | 0.2304          | 0.7986    | 0.8374 | 0.8175 | 0.9538   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2