---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: phobert-base-v2-finetuned-ner-thesis-dseb
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# phobert-base-v2-finetuned-ner-thesis-dseb

This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3842
- Precision: 0.75
- Recall: 0.8329
- F1: 0.7893
- Accuracy: 0.9491

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.0671        | 1.0   | 23   | 1.4715          | 0.0       | 0.0    | 0.0    | 0.6125   |
| 1.3776        | 2.0   | 46   | 0.9706          | 0.9471    | 0.3860 | 0.5485 | 0.7545   |
| 0.9833        | 3.0   | 69   | 0.6568          | 0.8144    | 0.7207 | 0.7647 | 0.9034   |
| 0.7423        | 4.0   | 92   | 0.4905          | 0.9215    | 0.9045 | 0.9130 | 0.9595   |
| 0.5928        | 5.0   | 115  | 0.3919          | 0.9626    | 0.9517 | 0.9572 | 0.9893   |
| 0.4955        | 6.0   | 138  | 0.3377          | 0.9658    | 0.9579 | 0.9619 | 0.9913   |
| 0.4013        | 7.0   | 161  | 0.3058          | 0.9658    | 0.9579 | 0.9619 | 0.9915   |
| 0.3747        | 8.0   | 184  | 0.2874          | 0.9658    | 0.9579 | 0.9619 | 0.9915   |
| 0.3618        | 9.0   | 207  | 0.2781          | 0.9658    | 0.9579 | 0.9619 | 0.9915   |
| 0.3477        | 10.0  | 230  | 0.2748          | 0.9658    | 0.9579 | 0.9619 | 0.9915   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0