---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: PhoBert_Lexical_Datasnet51KBoDuoiWithNewLexical
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/phunganhsang123/huggingface/runs/ueje0ytn)
# PhoBert_Lexical_Datasnet51KBoDuoiWithNewLexical

This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9059
- Accuracy: 0.848
- F1: 0.8469

## 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
- 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     |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
| No log        | 0.2506  | 200   | 0.6421          | 0.727    | 0.7274 |
| No log        | 0.5013  | 400   | 0.5851          | 0.745    | 0.7340 |
| No log        | 0.7519  | 600   | 0.5522          | 0.758    | 0.7561 |
| 0.3486        | 1.0025  | 800   | 0.5461          | 0.779    | 0.7693 |
| 0.3486        | 1.2531  | 1000  | 0.5009          | 0.786    | 0.7796 |
| 0.3486        | 1.5038  | 1200  | 0.5538          | 0.77     | 0.7681 |
| 0.3486        | 1.7544  | 1400  | 0.5067          | 0.777    | 0.7676 |
| 0.2582        | 2.0050  | 1600  | 0.5031          | 0.792    | 0.7842 |
| 0.2582        | 2.2556  | 1800  | 0.5232          | 0.786    | 0.7811 |
| 0.2582        | 2.5063  | 2000  | 0.5484          | 0.785    | 0.7827 |
| 0.2582        | 2.7569  | 2200  | 0.5155          | 0.798    | 0.7950 |
| 0.2134        | 3.0075  | 2400  | 0.5280          | 0.787    | 0.7847 |
| 0.2134        | 3.2581  | 2600  | 0.5263          | 0.793    | 0.7860 |
| 0.2134        | 3.5088  | 2800  | 0.5273          | 0.794    | 0.7930 |
| 0.2134        | 3.7594  | 3000  | 0.5354          | 0.792    | 0.7871 |
| 0.1799        | 4.0100  | 3200  | 0.5071          | 0.804    | 0.7982 |
| 0.1799        | 4.2607  | 3400  | 0.5655          | 0.802    | 0.7975 |
| 0.1799        | 4.5113  | 3600  | 0.5497          | 0.809    | 0.8034 |
| 0.1799        | 4.7619  | 3800  | 0.5645          | 0.808    | 0.8020 |
| 0.1493        | 5.0125  | 4000  | 0.5565          | 0.807    | 0.8003 |
| 0.1493        | 5.2632  | 4200  | 0.5861          | 0.813    | 0.8091 |
| 0.1493        | 5.5138  | 4400  | 0.6189          | 0.807    | 0.8055 |
| 0.1493        | 5.7644  | 4600  | 0.5003          | 0.819    | 0.8151 |
| 0.1278        | 6.0150  | 4800  | 0.5903          | 0.81     | 0.8080 |
| 0.1278        | 6.2657  | 5000  | 0.6112          | 0.81     | 0.8090 |
| 0.1278        | 6.5163  | 5200  | 0.5987          | 0.818    | 0.8150 |
| 0.1278        | 6.7669  | 5400  | 0.6466          | 0.802    | 0.8006 |
| 0.1091        | 7.0175  | 5600  | 0.6515          | 0.818    | 0.8172 |
| 0.1091        | 7.2682  | 5800  | 0.6541          | 0.819    | 0.8174 |
| 0.1091        | 7.5188  | 6000  | 0.6148          | 0.828    | 0.8246 |
| 0.1091        | 7.7694  | 6200  | 0.6246          | 0.827    | 0.8262 |
| 0.0917        | 8.0201  | 6400  | 0.6751          | 0.812    | 0.812  |
| 0.0917        | 8.2707  | 6600  | 0.6371          | 0.829    | 0.8281 |
| 0.0917        | 8.5213  | 6800  | 0.6979          | 0.823    | 0.8212 |
| 0.0917        | 8.7719  | 7000  | 0.6792          | 0.827    | 0.8241 |
| 0.0764        | 9.0226  | 7200  | 0.6833          | 0.835    | 0.8324 |
| 0.0764        | 9.2732  | 7400  | 0.6810          | 0.84     | 0.8383 |
| 0.0764        | 9.5238  | 7600  | 0.6799          | 0.826    | 0.8233 |
| 0.0764        | 9.7744  | 7800  | 0.6988          | 0.823    | 0.8211 |
| 0.0676        | 10.0251 | 8000  | 0.7213          | 0.832    | 0.8299 |
| 0.0676        | 10.2757 | 8200  | 0.7325          | 0.83     | 0.8288 |
| 0.0676        | 10.5263 | 8400  | 0.7338          | 0.831    | 0.8293 |
| 0.0676        | 10.7769 | 8600  | 0.7210          | 0.827    | 0.8262 |
| 0.0585        | 11.0276 | 8800  | 0.7874          | 0.829    | 0.8282 |
| 0.0585        | 11.2782 | 9000  | 0.7764          | 0.836    | 0.8347 |
| 0.0585        | 11.5288 | 9200  | 0.8112          | 0.835    | 0.8342 |
| 0.0585        | 11.7794 | 9400  | 0.7485          | 0.832    | 0.8307 |
| 0.0536        | 12.0301 | 9600  | 0.7640          | 0.835    | 0.8339 |
| 0.0536        | 12.2807 | 9800  | 0.7865          | 0.837    | 0.8344 |
| 0.0536        | 12.5313 | 10000 | 0.7579          | 0.836    | 0.8350 |
| 0.0536        | 12.7820 | 10200 | 0.7846          | 0.837    | 0.8357 |
| 0.0449        | 13.0326 | 10400 | 0.7385          | 0.84     | 0.8384 |
| 0.0449        | 13.2832 | 10600 | 0.8481          | 0.838    | 0.8372 |
| 0.0449        | 13.5338 | 10800 | 0.8422          | 0.838    | 0.8368 |
| 0.0449        | 13.7845 | 11000 | 0.7742          | 0.846    | 0.8445 |
| 0.0387        | 14.0351 | 11200 | 0.8304          | 0.842    | 0.8406 |
| 0.0387        | 14.2857 | 11400 | 0.7922          | 0.84     | 0.8386 |
| 0.0387        | 14.5363 | 11600 | 0.8284          | 0.843    | 0.8415 |
| 0.0387        | 14.7870 | 11800 | 0.8289          | 0.846    | 0.8449 |
| 0.0345        | 15.0376 | 12000 | 0.8012          | 0.849    | 0.8475 |
| 0.0345        | 15.2882 | 12200 | 0.8320          | 0.85     | 0.8489 |
| 0.0345        | 15.5388 | 12400 | 0.8118          | 0.853    | 0.8516 |
| 0.0345        | 15.7895 | 12600 | 0.8084          | 0.849    | 0.8480 |
| 0.0292        | 16.0401 | 12800 | 0.8427          | 0.841    | 0.8399 |
| 0.0292        | 16.2907 | 13000 | 0.8499          | 0.848    | 0.8469 |
| 0.0292        | 16.5414 | 13200 | 0.8477          | 0.848    | 0.8470 |
| 0.0292        | 16.7920 | 13400 | 0.8211          | 0.846    | 0.8441 |
| 0.0263        | 17.0426 | 13600 | 0.8418          | 0.848    | 0.8466 |
| 0.0263        | 17.2932 | 13800 | 0.8637          | 0.842    | 0.8408 |
| 0.0263        | 17.5439 | 14000 | 0.8693          | 0.845    | 0.8440 |
| 0.0263        | 17.7945 | 14200 | 0.8415          | 0.85     | 0.8483 |
| 0.0243        | 18.0451 | 14400 | 0.8735          | 0.848    | 0.8470 |
| 0.0243        | 18.2957 | 14600 | 0.8827          | 0.847    | 0.8460 |
| 0.0243        | 18.5464 | 14800 | 0.8729          | 0.85     | 0.8489 |
| 0.0243        | 18.7970 | 15000 | 0.8964          | 0.848    | 0.8469 |
| 0.0207        | 19.0476 | 15200 | 0.8939          | 0.846    | 0.8449 |
| 0.0207        | 19.2982 | 15400 | 0.9019          | 0.847    | 0.8460 |
| 0.0207        | 19.5489 | 15600 | 0.8948          | 0.848    | 0.8469 |
| 0.0207        | 19.7995 | 15800 | 0.9059          | 0.848    | 0.8469 |


### Framework versions

- Transformers 4.43.1
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1