readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - xnli
  metrics:
  - accuracy
  model-index:
  - name: bert-base-multilingual-cased-finetuned-nli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: xnli
        type: xnli
        config: en
        split: train
        args: en
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8156626506024096
  ---
  
  <!-- 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. -->
  
  # bert-base-multilingual-cased-finetuned-nli
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4681
  - Accuracy: 0.8157
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.9299        | 0.02  | 200   | 0.8468          | 0.6277   |
  | 0.7967        | 0.03  | 400   | 0.7425          | 0.6855   |
  | 0.7497        | 0.05  | 600   | 0.7116          | 0.6924   |
  | 0.7083        | 0.07  | 800   | 0.6868          | 0.7153   |
  | 0.6882        | 0.08  | 1000  | 0.6638          | 0.7289   |
  | 0.6944        | 0.1   | 1200  | 0.6476          | 0.7361   |
  | 0.6682        | 0.11  | 1400  | 0.6364          | 0.7458   |
  | 0.6635        | 0.13  | 1600  | 0.6592          | 0.7337   |
  | 0.6423        | 0.15  | 1800  | 0.6120          | 0.7510   |
  | 0.6196        | 0.16  | 2000  | 0.5990          | 0.7582   |
  | 0.6381        | 0.18  | 2200  | 0.6026          | 0.7538   |
  | 0.6276        | 0.2   | 2400  | 0.6054          | 0.7598   |
  | 0.6248        | 0.21  | 2600  | 0.6368          | 0.7526   |
  | 0.6331        | 0.23  | 2800  | 0.5959          | 0.7655   |
  | 0.6142        | 0.24  | 3000  | 0.6117          | 0.7554   |
  | 0.6124        | 0.26  | 3200  | 0.6221          | 0.7570   |
  | 0.6127        | 0.28  | 3400  | 0.5748          | 0.7695   |
  | 0.602         | 0.29  | 3600  | 0.5735          | 0.7598   |
  | 0.5923        | 0.31  | 3800  | 0.5609          | 0.7723   |
  | 0.5827        | 0.33  | 4000  | 0.5635          | 0.7743   |
  | 0.5732        | 0.34  | 4200  | 0.5547          | 0.7771   |
  | 0.5757        | 0.36  | 4400  | 0.5629          | 0.7739   |
  | 0.5736        | 0.37  | 4600  | 0.5680          | 0.7659   |
  | 0.5642        | 0.39  | 4800  | 0.5437          | 0.7871   |
  | 0.5763        | 0.41  | 5000  | 0.5589          | 0.7807   |
  | 0.5713        | 0.42  | 5200  | 0.5355          | 0.7867   |
  | 0.5644        | 0.44  | 5400  | 0.5346          | 0.7888   |
  | 0.5727        | 0.46  | 5600  | 0.5519          | 0.7815   |
  | 0.5539        | 0.47  | 5800  | 0.5219          | 0.7900   |
  | 0.5516        | 0.49  | 6000  | 0.5560          | 0.7795   |
  | 0.5539        | 0.51  | 6200  | 0.5544          | 0.7847   |
  | 0.5693        | 0.52  | 6400  | 0.5322          | 0.7932   |
  | 0.5632        | 0.54  | 6600  | 0.5404          | 0.7936   |
  | 0.565         | 0.55  | 6800  | 0.5382          | 0.7880   |
  | 0.5555        | 0.57  | 7000  | 0.5364          | 0.7920   |
  | 0.5329        | 0.59  | 7200  | 0.5177          | 0.7964   |
  | 0.54          | 0.6   | 7400  | 0.5286          | 0.7916   |
  | 0.554         | 0.62  | 7600  | 0.5401          | 0.7835   |
  | 0.5447        | 0.64  | 7800  | 0.5261          | 0.7876   |
  | 0.5438        | 0.65  | 8000  | 0.5032          | 0.8020   |
  | 0.5505        | 0.67  | 8200  | 0.5220          | 0.7924   |
  | 0.5364        | 0.68  | 8400  | 0.5398          | 0.7876   |
  | 0.5317        | 0.7   | 8600  | 0.5310          | 0.7944   |
  | 0.5361        | 0.72  | 8800  | 0.5297          | 0.7936   |
  | 0.5204        | 0.73  | 9000  | 0.5270          | 0.7940   |
  | 0.5189        | 0.75  | 9200  | 0.5193          | 0.7964   |
  | 0.5348        | 0.77  | 9400  | 0.5270          | 0.7867   |
  | 0.5363        | 0.78  | 9600  | 0.5194          | 0.7924   |
  | 0.5184        | 0.8   | 9800  | 0.5298          | 0.7888   |
  | 0.5072        | 0.81  | 10000 | 0.4999          | 0.7992   |
  | 0.5229        | 0.83  | 10200 | 0.4922          | 0.8108   |
  | 0.5201        | 0.85  | 10400 | 0.5019          | 0.7920   |
  | 0.5304        | 0.86  | 10600 | 0.4959          | 0.7992   |
  | 0.5061        | 0.88  | 10800 | 0.5047          | 0.7980   |
  | 0.5291        | 0.9   | 11000 | 0.4974          | 0.8068   |
  | 0.5099        | 0.91  | 11200 