---
library_name: transformers
license: agpl-3.0
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: cita_test
  results: []
---

<!-- 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. -->

# cita_test

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: 0.3031
- Accuracy: 0.9183
- F1: 0.9029
- Precision: 0.9113
- Recall: 0.8958

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6745        | 2.1277  | 100  | 0.2197          | 0.926    | 0.9110 | 0.9256    | 0.8996 |
| 0.2806        | 4.2553  | 200  | 0.2333          | 0.9293   | 0.9140 | 0.9363    | 0.8979 |
| 0.2806        | 6.3830  | 300  | 0.2366          | 0.916    | 0.9027 | 0.8998    | 0.9058 |
| 0.1669        | 8.5106  | 400  | 0.2277          | 0.9283   | 0.9149 | 0.9229    | 0.9080 |
| 0.1669        | 10.6383 | 500  | 0.2593          | 0.922    | 0.9067 | 0.9183    | 0.8972 |
| 0.1132        | 12.7660 | 600  | 0.2683          | 0.9243   | 0.9099 | 0.9191    | 0.9022 |
| 0.1132        | 14.8936 | 700  | 0.2796          | 0.9203   | 0.9051 | 0.9148    | 0.8969 |
| 0.0818        | 17.0213 | 800  | 0.2948          | 0.9217   | 0.9064 | 0.9175    | 0.8973 |
| 0.0818        | 19.1489 | 900  | 0.3031          | 0.9183   | 0.9029 | 0.9113    | 0.8958 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0