5CD-AI
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visobert-14gb-corpus

Fill-Mask

Transformers

Safetensors

xlm-roberta

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htdung167 commited on Apr 19

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 tags: []
 ---
+# 5CD-AI/viso-twhin-bert-large
+## Overview
 <!-- Provide a quick summary of what the model is/does. -->
+We reduce TwHIN-BERT's vocabulary size to 20k on the UIT dataset and continue pretraining for 10 epochs.
+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>90.92</td>
+            <td>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-pretrained</td>
+            <td>82.2</td>
+            <td>68.69</td>
+            <td>68.75</td>
+            <td>66.03</td>
+            <td>88.79</td>
+            <td>88.6</td>
+            <td>69.57</td>
+            <td>91.02</td>
+            <td>90.88</td>
+            <td>77.13</td>
+            <td>93.69</td>
+            <td>93.63</td>
+            <td>89.66</td>
+        </tr>
+        <tr align="center">
+            <td align="left">viso-twhin-bert-large</td>
+            <td><b>83.87</td>
+            <td><b>73.45</td>
+            <td><b>73.14</td>
+            <td><b>70.99</td>
+            <td><b>88.86</td>
+            <td><b>88.8</td>
+            <td><b>70.81</td>
+            <td><b>91.6</td>
+            <td><b>91.47</td>
+            <td><b>79.07</td>
+            <td><b>94.08</td>
+            <td><b>93.96</td>
+            <td><b>90.22</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-pretrained"
+mask_filler = pipeline("fill-mask", model_path)
+mask_filler("ăn nói xà <mask>", top_k=10)
+```