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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-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