---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v2.3
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: caner
      type: caner
      config: default
      split: train[85%:86%]
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8456375838926175
    - name: Recall
      type: recall
      value: 0.8456375838926175
    - name: F1
      type: f1
      value: 0.8456375838926175
    - name: Accuracy
      type: accuracy
      value: 0.9584533113944879
---

<!-- 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-v2.3

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: 0.2296
- Precision: 0.8456
- Recall: 0.8456
- F1: 0.8456
- Accuracy: 0.9585

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3219        | 1.0   | 3228 | 0.2632          | 0.7960    | 0.8054 | 0.8007 | 0.9383   |
| 0.2259        | 2.0   | 6456 | 0.2634          | 0.8189    | 0.8272 | 0.8230 | 0.9486   |
| 0.142         | 3.0   | 9684 | 0.2296          | 0.8456    | 0.8456 | 0.8456 | 0.9585   |


### Framework versions

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