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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- caner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-v4.010
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: caner
type: caner
config: default
split: train[90%:91%]
args: default
metrics:
- name: Precision
type: precision
value: 0.8621621621621621
- name: Recall
type: recall
value: 0.8715846994535519
- name: F1
type: f1
value: 0.8668478260869565
- name: Accuracy
type: accuracy
value: 0.9392893401015229
---
<!-- 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-v4.010
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: 0.3657
- Precision: 0.8622
- Recall: 0.8716
- F1: 0.8668
- Accuracy: 0.9393
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2718 | 1.0 | 3228 | 0.4023 | 0.8748 | 0.8019 | 0.8368 | 0.9265 |
| 0.2052 | 2.0 | 6456 | 0.3959 | 0.8243 | 0.8265 | 0.8254 | 0.9291 |
| 0.1584 | 3.0 | 9684 | 0.3657 | 0.8622 | 0.8716 | 0.8668 | 0.9393 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
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