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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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tags: []
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---
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# 5CD-AI/viso-twhin-bert-large
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## Overview
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<!-- Provide a quick summary of what the model is/does. -->
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We reduce TwHIN-BERT's vocabulary size to 20k on the UIT dataset and continue pretraining for 10 epochs.
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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):
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<table>
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<tr align="center">
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<td rowspan=2><b>Model</td>
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<td rowspan=2><b>Avg</td>
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<td colspan=3><b>Emotion Recognition</td>
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<td colspan=3><b>Hate Speech Detection</td>
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<td colspan=3><b>Spam Reviews Detection</td>
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<td colspan=3><b>Hate Speech Spans Detection</td>
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</tr>
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<tr align="center">
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<td><b>Acc</td>
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<td><b>WF1</td>
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<td><b>MF1</td>
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<td><b>Acc</td>
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<td><b>WF1</td>
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<td><b>MF1</td>
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<td><b>Acc</td>
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<td><b>WF1</td>
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<td><b>MF1</td>
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<td><b>Acc</td>
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<td><b>WF1</td>
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<td><b>MF1</td>
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</tr>
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<tr align="center">
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<td align="left">viBERT</td>
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<td>78.16</td>
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<td>61.91</td>
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<td>61.98</td>
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<td>59.7</td>
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<td>85.34</td>
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<td>85.01</td>
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<td>62.07</td>
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<td>89.93</td>
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<td>89.79</td>
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<td>76.8</td>
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<td>90.42</td>
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<td>90.45</td>
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<td>84.55</td>
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</tr>
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<tr align="center">
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<td align="left">vELECTRA</td>
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<td>79.23</td>
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<td>64.79</td>
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<td>64.71</td>
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<td>61.95</td>
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<td>86.96</td>
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<td>86.37</td>
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<td>63.95</td>
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<td>89.83</td>
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<td>89.68</td>
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<td>76.23</td>
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<td>90.59</td>
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<td>90.58</td>
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<td>85.12</td>
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</tr>
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<tr align="center">
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<td align="left">PhoBERT-Base </td>
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<td>79.3</td>
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<td>63.49</td>
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<td>63.36</td>
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<td>61.41</td>
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<td>87.12</td>
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<td>86.81</td>
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<td>65.01</td>
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<td>89.83</td>
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<td>89.75</td>
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<td>76.18</td>
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<td>91.32</td>
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<td>91.38</td>
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<td>85.92</td>
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</tr>
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<tr align="center">
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<td align="left">PhoBERT-Large</td>
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<td>79.82</td>
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<td>64.71</td>
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<td>64.66</td>
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<td>62.55</td>
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<td>87.32</td>
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<td>86.98</td>
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<td>65.14</td>
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<td>90.12</td>
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<td>90.03</td>
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<td>76.88</td>
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<td>91.44</td>
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<td>91.46</td>
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<td>86.56</td>
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</tr>
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<tr align="center">
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<td align="left">ViSoBERT</td>
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<td>81.58</td>
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<td>68.1</td>
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<td>68.37</td>
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<td>65.88</td>
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<td>88.51</td>
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<td>88.31</td>
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<td>68.77</td>
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<td>90.99</td>
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<td>90.92</td>
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<td>79.06</td>
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<td>91.62</td>
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<td>91.57</td>
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<td>86.8</td>
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</tr>
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<tr align="center">
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<td align="left">visobert-14gb-corpus-pretrained</td>
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<td>82.2</td>
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<td>68.69</td>
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<td>68.75</td>
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<td>66.03</td>
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<td>88.79</td>
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<td>88.6</td>
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<td>69.57</td>
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<td>91.02</td>
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<td>90.88</td>
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<td>77.13</td>
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<td>93.69</td>
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<td>93.63</td>
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<td>89.66</td>
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</tr>
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<tr align="center">
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<td align="left">viso-twhin-bert-large</td>
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<td><b>83.87</td>
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<td><b>73.45</td>
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<td><b>73.14</td>
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<td><b>70.99</td>
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<td><b>88.86</td>
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<td><b>88.8</td>
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<td><b>70.81</td>
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<td><b>91.6</td>
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<td><b>91.47</td>
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<td><b>79.07</td>
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<td><b>94.08</td>
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<td><b>93.96</td>
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<td><b>90.22</td>
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</tr>
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</div>
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</table>
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## Usage (HuggingFace Transformers)
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Install `transformers` package:
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pip install transformers
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Then you can use this model for fill-mask task like this:
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```python
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from transformers import pipeline
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model_path = "5CD-AI/viso-twhin-bert-large"
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mask_filler = pipeline("fill-mask", model_path)
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mask_filler("đúng nhận sai <mask>", top_k=10)
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```
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