---
tags:
- generated_from_trainer
datasets:
- udpos28
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: parsbert-finetuned-pos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: udpos28
      type: udpos28
      args: fa
    metrics:
    - name: Precision
      type: precision
      value: 0.9447937270415372
    - name: Recall
      type: recall
      value: 0.9486470191864382
    - name: F1
      type: f1
      value: 0.9467164522465448
    - name: Accuracy
      type: accuracy
      value: 0.9598951738759165
---

<!-- 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. -->

# parsbert-finetuned-pos

This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the udpos28 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1385
- Precision: 0.9448
- Recall: 0.9486
- F1: 0.9467
- Accuracy: 0.9599

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.122         | 1.0   | 3103 | 0.1215          | 0.9363    | 0.9424 | 0.9394 | 0.9561   |
| 0.0735        | 2.0   | 6206 | 0.1297          | 0.9413    | 0.9474 | 0.9443 | 0.9582   |
| 0.0373        | 3.0   | 9309 | 0.1385          | 0.9448    | 0.9486 | 0.9467 | 0.9599   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.11.6