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---
library_name: transformers
tags: []
pipeline_tag: fill-mask
widget:
- text: "shop làm ăn như cái <mask>"
- text: "hag từ Quảng <mask> kực nét"
- text: "Set xinh quá, <mask> bèo nhèo"
- text: "ăn nói xà <mask>"
---
# 5CD-AI/visobert-14gb-corpus
## Overview
<!-- Provide a quick summary of what the model is/does. -->
We continually pretrain `uitnlp/visobert` on a merged 14GB dataset, the training dataset includes:
- Internal data (100M comments and 15M posts on Facebook)
- UIT data, which is used to pretrain `uitnlp/visobert`
- MC4 ecommerce
Here are the results on 4 downstream tasks on Vietnamese social media texts, including Emotion Recognition(UIT-VSMEC), Hate Speech Detection(UIT-HSD), Spam Reviews Detection(ViSpamReviews), Hate Speech Spans Detection(ViHOS):
<table>
<tr align="center">
<td rowspan=2><b>Model</td>
<td rowspan=2><b>Avg</td>
<td colspan=3><b>Emotion Recognition</td>
<td colspan=3><b>Hate Speech Detection</td>
<td colspan=3><b>Spam Reviews Detection</td>
<td colspan=3><b>Hate Speech Spans Detection</td>
</tr>
<tr align="center">
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
<td><b>Acc</td>
<td><b>WF1</td>
<td><b>MF1</td>
</tr>
<tr align="center">
<td align="left">viBERT</td>
<td>78.16</td>
<td>61.91</td>
<td>61.98</td>
<td>59.7</td>
<td>85.34</td>
<td>85.01</td>
<td>62.07</td>
<td>89.93</td>
<td>89.79</td>
<td>76.8</td>
<td>90.42</td>
<td>90.45</td>
<td>84.55</td>
</tr>
<tr align="center">
<td align="left">vELECTRA</td>
<td>79.23</td>
<td>64.79</td>
<td>64.71</td>
<td>61.95</td>
<td>86.96</td>
<td>86.37</td>
<td>63.95</td>
<td>89.83</td>
<td>89.68</td>
<td>76.23</td>
<td>90.59</td>
<td>90.58</td>
<td>85.12</td>
</tr>
<tr align="center">
<td align="left">PhoBERT-Base </td>
<td>79.3</td>
<td>63.49</td>
<td>63.36</td>
<td>61.41</td>
<td>87.12</td>
<td>86.81</td>
<td>65.01</td>
<td>89.83</td>
<td>89.75</td>
<td>76.18</td>
<td>91.32</td>
<td>91.38</td>
<td>85.92</td>
</tr>
<tr align="center">
<td align="left">PhoBERT-Large</td>
<td>79.82</td>
<td>64.71</td>
<td>64.66</td>
<td>62.55</td>
<td>87.32</td>
<td>86.98</td>
<td>65.14</td>
<td>90.12</td>
<td>90.03</td>
<td>76.88</td>
<td>91.44</td>
<td>91.46</td>
<td>86.56</td>
</tr>
<tr align="center">
<td align="left">ViSoBERT</td>
<td>81.58</td>
<td>68.1</td>
<td>68.37</td>
<td>65.88</td>
<td>88.51</td>
<td>88.31</td>
<td>68.77</td>
<td>90.99</td>
<td><b>90.92</td>
<td><b>79.06</td>
<td>91.62</td>
<td>91.57</td>
<td>86.8</td>
</tr>
<tr align="center">
<td align="left">visobert-14gb-corpus</td>
<td><b>82.2</td>
<td><b>68.69</td>
<td><b>68.75</td>
<td><b>66.03</td>
<td><b>88.79</td>
<td><b>88.6</td>
<td><b>69.57</td>
<td><b>91.02</td>
<td>90.88</td>
<td>77.13</td>
<td><b>93.69</td>
<td><b>93.63</td>
<td><b>89.66</td>
</tr>
</div>
</table>
## Usage (HuggingFace Transformers)
Install `transformers` package:
pip install transformers
Then you can use this model for fill-mask task like this:
```python
from transformers import pipeline
model_path = "5CD-AI/visobert-14gb-corpus"
mask_filler = pipeline("fill-mask", model_path)
mask_filler("shop làm ăn như cái <mask>", top_k=10)
```
## Fine-tune Configuration
We fine-tune `5CD-AI/visobert-14gb-corpus` on 4 downstream tasks with `transformers` library with the following configuration:
- seed: 42
- gradient_accumulation_steps: 1
- weight_decay: 0.01
- optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
- training_epochs: 30
- model_max_length: 128
- learning_rate: 1e-5
- metric_for_best_model: wf1
- strategy: epoch
And different additional configurations for each task:
| Emotion Recognition | Hate Speech Detection | Spam Reviews Detection | Hate Speech Spans Detection |
| --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- |
|\- train_batch_size: 64<br>\- lr_scheduler_type: linear | \- train_batch_size: 32<br>\- lr_scheduler_type: linear | \- train_batch_size: 32<br>\- lr_scheduler_type: cosine | \- train_batch_size: 32<br>\- lr_scheduler_type: cosine |
|