readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - snips_built_in_intents
  metrics:
  - accuracy
  model-index:
  - name: roberta-base-finetuned-intent
    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. -->
  
  # roberta-base-finetuned-intent
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the snips_built_in_intents dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2720
  - Accuracy: 0.9333
  
  ## 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: 1
  - eval_batch_size: 1
  - seed: 42
  - distributed_type: IPU
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 8
  - total_eval_batch_size: 5
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - training precision: Mixed Precision
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.9568        | 1.0   | 37   | 1.7598          | 0.4333   |
  | 1.2238        | 2.0   | 74   | 0.8130          | 0.7667   |
  | 0.4536        | 3.0   | 111  | 0.4985          | 0.8      |
  | 0.2478        | 4.0   | 148  | 0.3535          | 0.8667   |
  | 0.0903        | 5.0   | 185  | 0.3110          | 0.8667   |
  | 0.0849        | 6.0   | 222  | 0.2720          | 0.9333   |
  | 0.0708        | 7.0   | 259  | 0.2742          | 0.8667   |
  | 0.0796        | 8.0   | 296  | 0.2839          | 0.8667   |
  | 0.0638        | 9.0   | 333  | 0.2949          | 0.8667   |
  | 0.0566        | 10.0  | 370  | 0.2925          | 0.8667   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.0+cpu
  - Datasets 2.7.1
  - Tokenizers 0.12.0
  