
## SBERT-Large on Kaggle Russian News Dataset

<table>
<thead>
  <tr>
    <th rowspan="4">Model</th>
    <th rowspan="4">Score<br></th>
    <th rowspan="4">Rank</th>
    <th colspan="12">Dataset</th>
  </tr>
  <tr>
    <td colspan="6">SentiRuEval-2016<br></td>
    <td colspan="2" rowspan="2">RuSentiment</td>
    <td rowspan="2">KRND</td>
    <td rowspan="2">LINIS Crowd</td>
    <td rowspan="2">RuTweetCorp</td>
    <td rowspan="2">RuReviews</td>
  </tr>
  <tr>
    <td colspan="3">TC</td>
    <td colspan="3">Banks</td>
  </tr>
  <tr>
    <td>micro F<sub>1</sub></td>
    <td>macro F<sub>1</sub></td>
    <td>F<sub>1</sub></td>
    <td>micro F<sub>1</sub></td>
    <td>macro F<sub>1</sub></td>
    <td>F<sub>1</sub></td>
    <td>wighted F<sub>1</sub></td>
    <td>F<sub>1</sub></td>
    <td>F<sub>1</sub></td>
    <td>F<sub>1</sub></td>
    <td>F<sub>1</sub></td>
    <td>F<sub>1</sub></td>
  </tr>
</thead>
<tbody>
  <tr>
    <td>SOTA</td>
    <td>n/s</td>
    <td></td>
    <td>76.71</td>
    <td>66.40</td>
    <td>70.68</td>
    <td>67.51</td>
    <td>69.53</td>
    <td>74.06</td>
    <td>78.50</td>
    <td>n/s</td>
    <td>73.63</td>
    <td>60.51</td>
    <td>83.68</td>
    <td>77.44</td>
  </tr>
  <tr>
    <td>XLM-RoBERTa-Large</td>
    <td>76.37</td>
    <td>1</td>
    <td>82.26</td>
    <td>76.36</td>
    <td>79.42</td>
    <td>76.35</td>
    <td>76.08</td>
    <td>80.89</td>
    <td>78.31</td>
    <td>75.27</td>
    <td>75.17</td>
    <td>60.03</td>
    <td>88.91</td>
    <td>78.81</td>
  </tr>
  <tr>
    <td>SBERT-Large</td>
    <td>75.43</td>
    <td>2</td>
    <td>78.40</td>
    <td>71.36</td>
    <td>75.14</td>
    <td>72.39</td>
    <td>71.87</td>
    <td>77.72</td>
    <td>78.58</td>
    <td>75.85</td>
    <td>74.20</td>
    <td>60.64</td>
    <td>88.66</td>
    <td>77.41</td>
  </tr>
  <tr>
    <td>MBARTRuSumGazeta</td>
    <td>74.70</td>
    <td>3</td>
    <td>76.06</td>
    <td>68.95</td>
    <td>73.04</td>
    <td>72.34</td>
    <td>71.93</td>
    <td>77.83</td>
    <td>76.71</td>
    <td>73.56</td>
    <td>74.18</td>
    <td>60.54</td>
    <td>87.22</td>
    <td>77.51</td>
  </tr>
  <tr>
    <td>Conversational RuBERT</td>
    <td>74.44</td>
    <td>4</td>
    <td>76.69</td>
    <td>69.09</td>
    <td>73.11</td>
    <td>69.44</td>
    <td>68.68</td>
    <td>75.56</td>
    <td>77.31</td>
    <td>74.40</td>
    <td>73.10</td>
    <td>59.95</td>
    <td>87.86</td>
    <td>77.78</td>
  </tr>
  <tr>
    <td>LaBSE</td>
    <td>74.11</td>
    <td>5</td>
    <td>77.00</td>
    <td>69.19</td>
    <td>73.55</td>
    <td>70.34</td>
    <td>69.83</td>
    <td>76.38</td>
    <td>74.94</td>
    <td>70.84</td>
    <td>73.20</td>
    <td>59.52</td>
    <td>87.89</td>
    <td>78.47</td>
  </tr>
  <tr>
    <td>XLM-RoBERTa-Base</td>
    <td>73.60</td>
    <td>6</td>
    <td>76.35</td>
    <td>69.37</td>
    <td>73.42</td>
    <td>68.45</td>
    <td>67.45</td>
    <td>74.05</td>
    <td>74.26</td>
    <td>70.44</td>
    <td>71.40</td>
    <td>60.19</td>
    <td>87.90</td>
    <td>78.28</td>
  </tr>
  <tr>
    <td>RuBERT</td>
    <td>73.45</td>
    <td>7</td>
    <td>74.03</td>
    <td>66.14</td>
    <td>70.75</td>
    <td>66.46</td>
    <td>66.40</td>
    <td>73.37</td>
    <td>75.49</td>
    <td>71.86</td>
    <td>72.15</td>
    <td>60.55</td>
    <td>86.99</td>
    <td>77.41</td>
  </tr>
  <tr>
    <td>MBART-50-Large-Many-to-Many</td>
    <td>73.15</td>
    <td>8</td>
    <td>75.38</td>
    <td>67.81</td>
    <td>72.26</td>
    <td>67.13</td>
    <td>66.97</td>
    <td>73.85</td>
    <td>74.78</td>
    <td>70.98</td>
    <td>71.98</td>
    <td>59.20</td>
    <td>87.05</td>
    <td>77.24</td>
  </tr>
  <tr>
    <td>SlavicBERT</td>
    <td>71.96</td>
    <td>9</td>
    <td>71.45</td>
    <td>63.03</td>
    <td>68.44</td>
    <td>64.32</td>
    <td>63.99</td>
    <td>71.31</td>
    <td>72.13</td>
    <td>67.57</td>
    <td>72.54</td>
    <td>58.70</td>
    <td>86.43</td>
    <td>77.16</td>
  </tr>
  <tr>
    <td>EnRuDR-BERT</td>
    <td>71.51</td>
    <td>10</td>
    <td>72.56</td>
    <td>64.74</td>
    <td>69.07</td>
    <td>61.44</td>
    <td>60.21</td>
    <td>68.34</td>
    <td>74.19</td>
    <td>69.94</td>
    <td>69.33</td>
    <td>56.55</td>
    <td>87.12</td>
    <td>77.95</td>
  </tr>
  <tr>
    <td>RuDR-BERT</td>
    <td>71.14</td>
    <td>11</td>
    <td>72.79</td>
    <td>64.23</td>
    <td>68.36</td>
    <td>61.86</td>
    <td>60.92</td>
    <td>68.48</td>
    <td>74.65</td>
    <td>70.63</td>
    <td>68.74</td>
    <td>54.45</td>
    <td>87.04</td>
    <td>77.91</td>
  </tr>
  <tr>
    <td>MBART-50-Large</td>
    <td>69.46</td>
    <td>12</td>
    <td>70.91</td>
    <td>62.67</td>
    <td>67.24</td>
    <td>61.12</td>
    <td>60.25</td>
    <td>68.41</td>
    <td>72.88</td>
    <td>68.63</td>
    <td>70.52</td>
    <td>46.39</td>
    <td>86.48</td>
    <td>77.52</td>
  </tr>
</tbody>
</table>


{"language": ["ru"], "tags": ["sentiment analysis", "Russian", "SBERT-Large"]}
transformers pytorch jax bert text-classification sentiment analysis Russian SBERT-Large ru autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #SBERT-Large #ru #autotrain_compatible #endpoints_compatible #region-us 

SBERT-Large on Kaggle Russian News Dataset
------------------------------------------




TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #SBERT-Large #ru #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #SBERT-Large #ru #autotrain_compatible #endpoints_compatible #region-us 
