  The uploaded model is from epoch 4 with Matthews Correlation of 61.05
  
  "best_metric": 0.4796141982078552,<br>
  "best_model_checkpoint": "/content/output_dir/checkpoint-268",<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": 7113018526540800.0,<br>
  "trial_name": null,<br>
  "trial_params": null<br>
<table class="table table-bordered table-hover table-condensed" style="width: 60%; overflow: auto">
<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="left">1</td>
<td align="left">0.4796141982078552</td>
<td align="left">0.5351033849356494</td>
<td align="left">8.8067</td>
<td align="left">118.433</td>
<td align="left">14.875</td>
<td align="left">268</td>
<td align="left">0.000018067415730337083</td>
<td align="left">0.4913</td>
</tr>
<tr>
<td align="left">2</td>
<td align="left">0.5334435701370239</td>
<td align="left">0.5178799252679331</td>
<td align="left">8.9439</td>
<td align="left">116.616</td>
<td align="left">14.647</td>
<td align="left">536</td>
<td align="left">0.00001605992509363296</td>
<td align="left">0.2872</td>
</tr>
<tr>
<td align="left">3</td>
<td align="left">0.5544090270996094</td>
<td align="left">0.5649788851042796</td>
<td align="left">8.9467</td>
<td align="left">116.58</td>
<td align="left">14.642</td>
<td align="left">804</td>
<td align="left">0.000014052434456928841</td>
<td align="left">0.1777</td>
</tr>
<tr>
<td align="left">4</td>
<td align="left">0.5754779577255249</td>
<td align="left">0.6105374636148787</td>
<td align="left">8.8982</td>
<td align="left">117.215</td>
<td align="left">14.722</td>
<td align="left">1072</td>
<td align="left">0.000012044943820224718</td>
<td align="left">0.1263</td>
</tr>
<tr>
<td align="left">5</td>
<td align="left">0.7263916730880737</td>
<td align="left">0.5807606001872874</td>
<td align="left">8.9705</td>
<td align="left">116.27</td>
<td align="left">14.603</td>
<td align="left">1340</td>
<td align="left">0.000010037453183520601</td>
<td align="left">0.0905</td>
</tr>
<tr>
<td align="left">6</td>
<td align="left">0.8121512532234192</td>
<td align="left">0.5651092792103851</td>
<td align="left">8.9924</td>
<td align="left">115.987</td>
<td align="left">14.568</td>
<td align="left">1608</td>
<td align="left">0.00000802996254681648</td>
<td align="left">0.0692</td>
</tr>
<tr>
<td align="left">7</td>
<td align="left">0.941014289855957</td>
<td align="left">0.5632084517291658</td>
<td align="left">8.9583</td>
<td align="left">116.428</td>
<td align="left">14.623</td>
<td align="left">1876</td>
<td align="left">0.000006022471910112359</td>
<td align="left">0.0413</td>
</tr>
<tr>
<td align="left">8</td>
<td align="left">1.0095174312591553</td>
<td align="left">0.5856531698367675</td>
<td align="left">9.0029</td>
<td align="left">115.851</td>
<td align="left">14.551</td>
<td align="left">2144</td>
<td align="left">0.00000401498127340824</td>
<td align="left">0.0327</td>
</tr>
<tr>
<td align="left">9</td>
<td align="left">1.0425965785980225</td>
<td align="left">0.5941395545037332</td>
<td align="left">8.9217</td>
<td align="left">116.906</td>
<td align="left">14.683</td>
<td align="left">2412</td>
<td align="left">0.00000200749063670412</td>
<td align="left">0.0202</td>
</tr>
<tr>
<td align="left">10</td>
<td align="left">1.0782166719436646</td>
<td align="left">0.5956649094312695</td>
<td align="left">8.9472</td>
<td align="left">116.572</td>
<td align="left">14.641</td>
<td align="left">2680</td>
<td align="left">0</td>
<td align="left">0.0104</td>
</tr>
</tbody></table>
{}
transformers pytorch bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

  The uploaded model is from epoch 4 with Matthews Correlation of 61.05
  
  "best_metric": 0.4796141982078552,<br>
  "best_model_checkpoint": "/content/output_dir/checkpoint-268",<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": 7113018526540800.0,<br>
  "trial_name": null,<br>
  "trial_params": null<br>
<table class="table table-bordered table-hover table-condensed" style="width: 60%; overflow: auto">
<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="left">1</td>
<td align="left">0.4796141982078552</td>
<td align="left">0.5351033849356494</td>
<td align="left">8.8067</td>
<td align="left">118.433</td>
<td align="left">14.875</td>
<td align="left">268</td>
<td align="left">0.000018067415730337083</td>
<td align="left">0.4913</td>
</tr>
<tr>
<td align="left">2</td>
<td align="left">0.5334435701370239</td>
<td align="left">0.5178799252679331</td>
<td align="left">8.9439</td>
<td align="left">116.616</td>
<td align="left">14.647</td>
<td align="left">536</td>
<td align="left">0.00001605992509363296</td>
<td align="left">0.2872</td>
</tr>
<tr>
<td align="left">3</td>
<td align="left">0.5544090270996094</td>
<td align="left">0.5649788851042796</td>
<td align="left">8.9467</td>
<td align="left">116.58</td>
<td align="left">14.642</td>
<td align="left">804</td>
<td align="left">0.000014052434456928841</td>
<td align="left">0.1777</td>
</tr>
<tr>
<td align="left">4</td>
<td align="left">0.5754779577255249</td>
<td align="left">0.6105374636148787</td>
<td align="left">8.8982</td>
<td align="left">117.215</td>
<td align="left">14.722</td>
<td align="left">1072</td>
<td align="left">0.000012044943820224718</td>
<td align="left">0.1263</td>
</tr>
<tr>
<td align="left">5</td>
<td align="left">0.7263916730880737</td>
<td align="left">0.5807606001872874</td>
<td align="left">8.9705</td>
<td align="left">116.27</td>
<td align="left">14.603</td>
<td align="left">1340</td>
<td align="left">0.000010037453183520601</td>
<td align="left">0.0905</td>
</tr>
<tr>
<td align="left">6</td>
<td align="left">0.8121512532234192</td>
<td align="left">0.5651092792103851</td>
<td align="left">8.9924</td>
<td align="left">115.987</td>
<td align="left">14.568</td>
<td align="left">1608</td>
<td align="left">0.00000802996254681648</td>
<td align="left">0.0692</td>
</tr>
<tr>
<td align="left">7</td>
<td align="left">0.941014289855957</td>
<td align="left">0.5632084517291658</td>
<td align="left">8.9583</td>
<td align="left">116.428</td>
<td align="left">14.623</td>
<td align="left">1876</td>
<td align="left">0.000006022471910112359</td>
<td align="left">0.0413</td>
</tr>
<tr>
<td align="left">8</td>
<td align="left">1.0095174312591553</td>
<td align="left">0.5856531698367675</td>
<td align="left">9.0029</td>
<td align="left">115.851</td>
<td align="left">14.551</td>
<td align="left">2144</td>
<td align="left">0.00000401498127340824</td>
<td align="left">0.0327</td>
</tr>
<tr>
<td align="left">9</td>
<td align="left">1.0425965785980225</td>
<td align="left">0.5941395545037332</td>
<td align="left">8.9217</td>
<td align="left">116.906</td>
<td align="left">14.683</td>
<td align="left">2412</td>
<td align="left">0.00000200749063670412</td>
<td align="left">0.0202</td>
</tr>
<tr>
<td align="left">10</td>
<td align="left">1.0782166719436646</td>
<td align="left">0.5956649094312695</td>
<td align="left">8.9472</td>
<td align="left">116.572</td>
<td align="left">14.641</td>
<td align="left">2680</td>
<td align="left">0</td>
<td align="left">0.0104</td>
</tr>
</tbody></table>
TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

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