readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: sbert_large-finetuned-sent_in_news_sents_3lab
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # sbert_large-finetuned-sent_in_news_sents_3lab
  
  This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9443
  - Accuracy: 0.8580
  - F1: 0.6199
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 17
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | No log        | 1.0   | 264  | 0.6137          | 0.8608   | 0.3084 |
  | 0.524         | 2.0   | 528  | 0.6563          | 0.8722   | 0.4861 |
  | 0.524         | 3.0   | 792  | 0.7110          | 0.8494   | 0.4687 |
  | 0.2225        | 4.0   | 1056 | 0.7323          | 0.8608   | 0.6015 |
  | 0.2225        | 5.0   | 1320 | 0.9604          | 0.8551   | 0.6185 |
  | 0.1037        | 6.0   | 1584 | 0.8801          | 0.8523   | 0.5535 |
  | 0.1037        | 7.0   | 1848 | 0.9443          | 0.8580   | 0.6199 |
  | 0.0479        | 8.0   | 2112 | 1.0048          | 0.8608   | 0.6168 |
  | 0.0479        | 9.0   | 2376 | 0.9757          | 0.8551   | 0.6097 |
  | 0.0353        | 10.0  | 2640 | 1.0743          | 0.8580   | 0.6071 |
  | 0.0353        | 11.0  | 2904 | 1.1216          | 0.8580   | 0.6011 |
  
  
  ### Framework versions
  
  - Transformers 4.11.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  