  The uploaded model is from epoch 9 with Matthews Correlation of 66.77
 
  "best_metric": 0.667660908939119,<br>
  "best_model_checkpoint": "/content/output_dir/checkpoint-2412",<br>
  "epoch": 10.0,<br>
  "global_step": 2680,<br>
  "is_hyper_param_search": false,<br>
  "is_local_process_zero": true,<br>
  "is_world_process_zero": true,<br>
  "max_steps": 2680,<br>
  "num_train_epochs": 10,<br>
  "total_flos": 7189983634007040.0,<br>
  "trial_name": null,<br>
  "trial_params": null<br>
<table class="table table-bordered table-hover table-condensed">
<thead><tr><th title="Field #1">epoch</th>
<th title="Field #2">eval_loss</th>
<th title="Field #3">eval_matthews_correlation</th>
<th title="Field #4">eval_runtime</th>
<th title="Field #5">eval_samples_per_second</th>
<th title="Field #6">eval_steps_per_second</th>
<th title="Field #7">step</th>
<th title="Field #8">learning_rate</th>
<th title="Field #9">loss</th>
</tr></thead>
<tbody><tr>
<td align="right">1</td>
<td align="right">0.5115634202957153</td>
<td align="right">0.5385290213636863</td>
<td align="right">7.985</td>
<td align="right">130.62</td>
<td align="right">16.406</td>
<td align="right">268</td>
<td align="right">0.00009280492497114274</td>
<td align="right">0.4622</td>
</tr>
<tr>
<td align="right">2</td>
<td align="right">0.4201788902282715</td>
<td align="right">0.6035894895952164</td>
<td align="right">8.0283</td>
<td align="right">129.916</td>
<td align="right">16.317</td>
<td align="right">536</td>
<td align="right">0.00008249326664101577</td>
<td align="right">0.2823</td>
</tr>
<tr>
<td align="right">3</td>
<td align="right">0.580650806427002</td>
<td align="right">0.5574138665741355</td>
<td align="right">8.1314</td>
<td align="right">128.268</td>
<td align="right">16.11</td>
<td align="right">804</td>
<td align="right">0.00007218160831088881</td>
<td align="right">0.1804</td>
</tr>
<tr>
<td align="right">4</td>
<td align="right">0.4439031779766083</td>
<td align="right">0.6557697896854868</td>
<td align="right">8.1435</td>
<td align="right">128.078</td>
<td align="right">16.087</td>
<td align="right">1072</td>
<td align="right">0.00006186994998076183</td>
<td align="right">0.1357</td>
</tr>
<tr>
<td align="right">5</td>
<td align="right">0.5736830830574036</td>
<td align="right">0.6249925495853809</td>
<td align="right">8.0533</td>
<td align="right">129.512</td>
<td align="right">16.267</td>
<td align="right">1340</td>
<td align="right">0.00005155829165063486</td>
<td align="right">0.0913</td>
</tr>
<tr>
<td align="right">6</td>
<td align="right">0.7729296684265137</td>
<td align="right">0.6188970025554703</td>
<td align="right">8.081</td>
<td align="right">129.068</td>
<td align="right">16.211</td>
<td align="right">1608</td>
<td align="right">0.000041246633320507885</td>
<td align="right">0.065</td>
</tr>
<tr>
<td align="right">7</td>
<td align="right">0.7351673245429993</td>
<td align="right">0.6405767700619004</td>
<td align="right">8.1372</td>
<td align="right">128.176</td>
<td align="right">16.099</td>
<td align="right">1876</td>
<td align="right">0.00003093497499038092</td>
<td align="right">0.0433</td>
</tr>
<tr>
<td align="right">8</td>
<td align="right">0.7900031208992004</td>
<td align="right">0.6565021466238845</td>
<td align="right">8.1095</td>
<td align="right">128.615</td>
<td align="right">16.154</td>
<td align="right">2144</td>
<td align="right">0.000020623316660253942</td>
<td align="right">0.0199</td>
</tr>
<tr>
<td align="right">9</td>
<td align="right">0.8539554476737976</td>
<td align="right">0.667660908939119</td>
<td align="right">8.1204</td>
<td align="right">128.442</td>
<td align="right">16.132</td>
<td align="right">2412</td>
<td align="right">0.000010311658330126971</td>
<td align="right">0.0114</td>
</tr>
<tr>
<td align="right">10</td>
<td align="right">0.9261117577552795</td>
<td align="right">0.660301076782038</td>
<td align="right">8.0088</td>
<td align="right">130.231</td>
<td align="right">16.357</td>
<td align="right">2680</td>
<td align="right">0</td>
<td align="right">0.0066</td>
</tr>
</tbody></table>
{}
transformers pytorch electra text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 

  The uploaded model is from epoch 9 with Matthews Correlation of 66.77
 
  "best_metric": 0.667660908939119,<br>
  "best_model_checkpoint": "/content/output_dir/checkpoint-2412",<br>
  "epoch": 10.0,<br>
  "global_step": 2680,<br>
  "is_hyper_param_search": false,<br>
  "is_local_process_zero": true,<br>
  "is_world_process_zero": true,<br>
  "max_steps": 2680,<br>
  "num_train_epochs": 10,<br>
  "total_flos": 7189983634007040.0,<br>
  "trial_name": null,<br>
  "trial_params": null<br>
<table class="table table-bordered table-hover table-condensed">
<thead><tr><th title="Field #1">epoch</th>
<th title="Field #2">eval_loss</th>
<th title="Field #3">eval_matthews_correlation</th>
<th title="Field #4">eval_runtime</th>
<th title="Field #5">eval_samples_per_second</th>
<th title="Field #6">eval_steps_per_second</th>
<th title="Field #7">step</th>
<th title="Field #8">learning_rate</th>
<th title="Field #9">loss</th>
</tr></thead>
<tbody><tr>
<td align="right">1</td>
<td align="right">0.5115634202957153</td>
<td align="right">0.5385290213636863</td>
<td align="right">7.985</td>
<td align="right">130.62</td>
<td align="right">16.406</td>
<td align="right">268</td>
<td align="right">0.00009280492497114274</td>
<td align="right">0.4622</td>
</tr>
<tr>
<td align="right">2</td>
<td align="right">0.4201788902282715</td>
<td align="right">0.6035894895952164</td>
<td align="right">8.0283</td>
<td align="right">129.916</td>
<td align="right">16.317</td>
<td align="right">536</td>
<td align="right">0.00008249326664101577</td>
<td align="right">0.2823</td>
</tr>
<tr>
<td align="right">3</td>
<td align="right">0.580650806427002</td>
<td align="right">0.5574138665741355</td>
<td align="right">8.1314</td>
<td align="right">128.268</td>
<td align="right">16.11</td>
<td align="right">804</td>
<td align="right">0.00007218160831088881</td>
<td align="right">0.1804</td>
</tr>
<tr>
<td align="right">4</td>
<td align="right">0.4439031779766083</td>
<td align="right">0.6557697896854868</td>
<td align="right">8.1435</td>
<td align="right">128.078</td>
<td align="right">16.087</td>
<td align="right">1072</td>
<td align="right">0.00006186994998076183</td>
<td align="right">0.1357</td>
</tr>
<tr>
<td align="right">5</td>
<td align="right">0.5736830830574036</td>
<td align="right">0.6249925495853809</td>
<td align="right">8.0533</td>
<td align="right">129.512</td>
<td align="right">16.267</td>
<td align="right">1340</td>
<td align="right">0.00005155829165063486</td>
<td align="right">0.0913</td>
</tr>
<tr>
<td align="right">6</td>
<td align="right">0.7729296684265137</td>
<td align="right">0.6188970025554703</td>
<td align="right">8.081</td>
<td align="right">129.068</td>
<td align="right">16.211</td>
<td align="right">1608</td>
<td align="right">0.000041246633320507885</td>
<td align="right">0.065</td>
</tr>
<tr>
<td align="right">7</td>
<td align="right">0.7351673245429993</td>
<td align="right">0.6405767700619004</td>
<td align="right">8.1372</td>
<td align="right">128.176</td>
<td align="right">16.099</td>
<td align="right">1876</td>
<td align="right">0.00003093497499038092</td>
<td align="right">0.0433</td>
</tr>
<tr>
<td align="right">8</td>
<td align="right">0.7900031208992004</td>
<td align="right">0.6565021466238845</td>
<td align="right">8.1095</td>
<td align="right">128.615</td>
<td align="right">16.154</td>
<td align="right">2144</td>
<td align="right">0.000020623316660253942</td>
<td align="right">0.0199</td>
</tr>
<tr>
<td align="right">9</td>
<td align="right">0.8539554476737976</td>
<td align="right">0.667660908939119</td>
<td align="right">8.1204</td>
<td align="right">128.442</td>
<td align="right">16.132</td>
<td align="right">2412</td>
<td align="right">0.000010311658330126971</td>
<td align="right">0.0114</td>
</tr>
<tr>
<td align="right">10</td>
<td align="right">0.9261117577552795</td>
<td align="right">0.660301076782038</td>
<td align="right">8.0088</td>
<td align="right">130.231</td>
<td align="right">16.357</td>
<td align="right">2680</td>
<td align="right">0</td>
<td align="right">0.0066</td>
</tr>
</tbody></table>
TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us 
