### Model Description
- **Developed by:** Mikail Doğruer
- **Model type:** 128k Bert Model
- **Language(s) (NLP):** Turkish

{'eval_loss': 0.25295722484588623,
 'eval_accuracy': 0.9484536082474226,
 'eval_precision': 0.9497797985940278,
 'eval_recall': 0.9463618552943321,
 'eval_f1': 0.9479608415152083,
 'eval_runtime': 1.5618,
 'eval_samples_per_second': 186.318,
 'eval_steps_per_second': 1.921,
 'epoch': 12.0}

  #     Epoch	Training Loss  Validation Loss	Accuracy	Precision	Recall	F1
* ## 1	0.297100	0.253670	0.945017	0.945524	0.942778	0.944051
* ## 2	0.279300	0.253695	0.945017	0.945524	0.942778	0.944051
* ## 3	0.278800	0.253687	0.945017	0.945524	0.942778	0.944051
* ## 4	0.299700	0.253546	0.948454	0.949780	0.946362	0.947961
* ## 5	0.293400	0.253468	0.948454	0.949780	0.946362	0.947961
* ## 6	0.316400	0.253449	0.948454	0.949780	0.946362	0.947961
* ## 7	0.285500	0.253279	0.948454	0.949780	0.946362	0.947961
* ## 8	0.326200	0.253156	0.948454	0.949780	0.946362	0.947961
* ## 9	0.297200	0.253054	0.948454	0.949780	0.946362	0.947961
* ## 10	0.284200	0.252992	0.948454	0.949780	0.946362	0.947961
* ## 11	0.289500	0.252961	0.948454	0.949780	0.946362	0.947961
* ## 12	0.285400	0.252957	0.948454	0.949780	0.946362	0.947961
  
** TrainOutput(global_step=120, training_loss=0.29438418547312417, metrics={'train_runtime': 253.1978, 'train_samples_per_second': 55.072,
'train_steps_per_second': 0.474, 'total_flos': 716572948593600.0, 'train_loss': 0.29438418547312417, 'epoch': 12.0})


{"language": ["tr"], "license": "cc", "library_name": "bertopic", "tags": ["finance"], "metrics": ["accuracy", "precision", "recall", "f1"], "pipeline_tag": "text-classification"}
bertopic pytorch bert finance text-classification tr license:cc has_space region:us
TAGS
#bertopic #pytorch #bert #finance #text-classification #tr #license-cc #has_space #region-us 

### Model Description
- Developed by: Mikail Doğruer
- Model type: 128k Bert Model
- Language(s) (NLP): Turkish

{'eval_loss': 0.25295722484588623,
 'eval_accuracy': 0.9484536082474226,
 'eval_precision': 0.9497797985940278,
 'eval_recall': 0.9463618552943321,
 'eval_f1': 0.9479608415152083,
 'eval_runtime': 1.5618,
 'eval_samples_per_second': 186.318,
 'eval_steps_per_second': 1.921,
 'epoch': 12.0}

  #     Epoch	Training Loss  Validation Loss	Accuracy	Precision	Recall	F1
* ## 1	0.297100	0.253670	0.945017	0.945524	0.942778	0.944051
* ## 2	0.279300	0.253695	0.945017	0.945524	0.942778	0.944051
* ## 3	0.278800	0.253687	0.945017	0.945524	0.942778	0.944051
* ## 4	0.299700	0.253546	0.948454	0.949780	0.946362	0.947961
* ## 5	0.293400	0.253468	0.948454	0.949780	0.946362	0.947961
* ## 6	0.316400	0.253449	0.948454	0.949780	0.946362	0.947961
* ## 7	0.285500	0.253279	0.948454	0.949780	0.946362	0.947961
* ## 8	0.326200	0.253156	0.948454	0.949780	0.946362	0.947961
* ## 9	0.297200	0.253054	0.948454	0.949780	0.946362	0.947961
* ## 10	0.284200	0.252992	0.948454	0.949780	0.946362	0.947961
* ## 11	0.289500	0.252961	0.948454	0.949780	0.946362	0.947961
* ## 12	0.285400	0.252957	0.948454	0.949780	0.946362	0.947961
  
 TrainOutput(global_step=120, training_loss=0.29438418547312417, metrics={'train_runtime': 253.1978, 'train_samples_per_second': 55.072,
'train_steps_per_second': 0.474, 'total_flos': 716572948593600.0, 'train_loss': 0.29438418547312417, 'epoch': 12.0})


### Model Description
- Developed by: Mikail Doğruer
- Model type: 128k Bert Model
- Language(s) (NLP): Turkish

{'eval_loss': 0.25295722484588623,
 'eval_accuracy': 0.9484536082474226,
 'eval_precision': 0.9497797985940278,
 'eval_recall': 0.9463618552943321,
 'eval_f1': 0.9479608415152083,
 'eval_runtime': 1.5618,
 'eval_samples_per_second': 186.318,
 'eval_steps_per_second': 1.921,
 'epoch': 12.0}

  #     Epoch	Training Loss  Validation Loss	Accuracy	Precision	Recall	F1
* ## 1	0.297100	0.253670	0.945017	0.945524	0.942778	0.944051
* ## 2	0.279300	0.253695	0.945017	0.945524	0.942778	0.944051
* ## 3	0.278800	0.253687	0.945017	0.945524	0.942778	0.944051
* ## 4	0.299700	0.253546	0.948454	0.949780	0.946362	0.947961
* ## 5	0.293400	0.253468	0.948454	0.949780	0.946362	0.947961
* ## 6	0.316400	0.253449	0.948454	0.949780	0.946362	0.947961
* ## 7	0.285500	0.253279	0.948454	0.949780	0.946362	0.947961
* ## 8	0.326200	0.253156	0.948454	0.949780	0.946362	0.947961
* ## 9	0.297200	0.253054	0.948454	0.949780	0.946362	0.947961
* ## 10	0.284200	0.252992	0.948454	0.949780	0.946362	0.947961
* ## 11	0.289500	0.252961	0.948454	0.949780	0.946362	0.947961
* ## 12	0.285400	0.252957	0.948454	0.949780	0.946362	0.947961
  
 TrainOutput(global_step=120, training_loss=0.29438418547312417, metrics={'train_runtime': 253.1978, 'train_samples_per_second': 55.072,
'train_steps_per_second': 0.474, 'total_flos': 716572948593600.0, 'train_loss': 0.29438418547312417, 'epoch': 12.0})
TAGS
#bertopic #pytorch #bert #finance #text-classification #tr #license-cc #has_space #region-us 
 ### Model Description
- Developed by: Mikail Doğruer
- Model type: 128k Bert Model
- Language(s) (NLP): Turkish

{'eval_loss': 0.25295722484588623,
 'eval_accuracy': 0.9484536082474226,
 'eval_precision': 0.9497797985940278,
 'eval_recall': 0.9463618552943321,
 'eval_f1': 0.9479608415152083,
 'eval_runtime': 1.5618,
 'eval_samples_per_second': 186.318,
 'eval_steps_per_second': 1.921,
 'epoch': 12.0}

  #     Epoch	Training Loss  Validation Loss	Accuracy	Precision	Recall	F1
* ## 1	0.297100	0.253670	0.945017	0.945524	0.942778	0.944051
* ## 2	0.279300	0.253695	0.945017	0.945524	0.942778	0.944051
* ## 3	0.278800	0.253687	0.945017	0.945524	0.942778	0.944051
* ## 4	0.299700	0.253546	0.948454	0.949780	0.946362	0.947961
* ## 5	0.293400	0.253468	0.948454	0.949780	0.946362	0.947961
* ## 6	0.316400	0.253449	0.948454	0.949780	0.946362	0.947961
* ## 7	0.285500	0.253279	0.948454	0.949780	0.946362	0.947961
* ## 8	0.326200	0.253156	0.948454	0.949780	0.946362	0.947961
* ## 9	0.297200	0.253054	0.948454	0.949780	0.946362	0.947961
* ## 10	0.284200	0.252992	0.948454	0.949780	0.946362	0.947961
* ## 11	0.289500	0.252961	0.948454	0.949780	0.946362	0.947961
* ## 12	0.285400	0.252957	0.948454	0.949780	0.946362	0.947961
  
 TrainOutput(global_step=120, training_loss=0.29438418547312417, metrics={'train_runtime': 253.1978, 'train_samples_per_second': 55.072,
'train_steps_per_second': 0.474, 'total_flos': 716572948593600.0, 'train_loss': 0.29438418547312417, 'epoch': 12.0})