readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: vxnli-v0
    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. -->
  
  # vxnli-v0
  
  This model is a fine-tuned version of [microsoft/tapex-base-finetuned-wtq](https://huggingface.co/microsoft/tapex-base-finetuned-wtq) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3409
  - Exact Match: 0.6371
  
  ## 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: 5e-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: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Exact Match |
  |:-------------:|:-----:|:----:|:---------------:|:-----------:|
  | 0.2238        | 1.0   | 400  | 0.3173          | 0.5495      |
  | 0.0189        | 2.0   | 800  | 0.3454          | 0.5769      |
  | 0.0117        | 3.0   | 1200 | 0.3820          | 0.5509      |
  | 0.0077        | 4.0   | 1600 | 0.3753          | 0.5769      |
  | 0.0063        | 5.0   | 2000 | 0.3912          | 0.5935      |
  | 0.0054        | 6.0   | 2400 | 0.3932          | 0.5906      |
  | 0.0052        | 7.0   | 2800 | 0.3967          | 0.5740      |
  | 0.0049        | 8.0   | 3200 | 0.3899          | 0.6101      |
  | 0.004         | 9.0   | 3600 | 0.4111          | 0.5978      |
  | 0.0045        | 10.0  | 4000 | 0.4381          | 0.5870      |
  | 0.0038        | 11.0  | 4400 | 0.5001          | 0.5617      |
  | 0.0036        | 12.0  | 4800 | 0.4930          | 0.5834      |
  | 0.0044        | 13.0  | 5200 | 0.4405          | 0.5639      |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  