readme: ---
  license: mit
  tags:
  - generated_from_trainer
  - nlu
  - intent-classification
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mdeberta-v3-base_amazon-massive_intent
    results:
    - task: 
        name: intent-classification
        type: intent-classification  
      dataset:
        name: MASSIVE
        type: AmazonScience/massive
        split: test
      metrics:
        - name: F1
          type: f1
          value: 0.8136
  datasets:
  - AmazonScience/massive
  language:
  - en
  pipeline_tag: text-classification
  ---
  
  <!-- 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. -->
  
  # mdeberta-v3-base_amazon-massive_intent
  
  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1661
  - Accuracy: 0.8136
  - F1: 0.8136
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
  | 3.6412        | 1.0   | 720   | 2.7536          | 0.3123   | 0.3123 |
  | 2.8575        | 2.0   | 1440  | 1.8556          | 0.5303   | 0.5303 |
  | 1.7284        | 3.0   | 2160  | 1.3758          | 0.6699   | 0.6699 |
  | 1.3794        | 4.0   | 2880  | 1.1221          | 0.7236   | 0.7236 |
  | 0.942         | 5.0   | 3600  | 0.9936          | 0.7609   | 0.7609 |
  | 0.7672        | 6.0   | 4320  | 0.9411          | 0.7727   | 0.7727 |
  | 0.602         | 7.0   | 5040  | 0.9196          | 0.7841   | 0.7841 |
  | 0.4776        | 8.0   | 5760  | 0.9328          | 0.7895   | 0.7895 |
  | 0.4347        | 9.0   | 6480  | 0.9602          | 0.7860   | 0.7860 |
  | 0.2941        | 10.0  | 7200  | 0.9543          | 0.7949   | 0.7949 |
  | 0.2783        | 11.0  | 7920  | 0.9979          | 0.8013   | 0.8013 |
  | 0.2038        | 12.0  | 8640  | 0.9702          | 0.8062   | 0.8062 |
  | 0.1827        | 13.0  | 9360  | 1.0121          | 0.8106   | 0.8106 |
  | 0.1352        | 14.0  | 10080 | 1.0339          | 0.8136   | 0.8136 |
  | 0.1115        | 15.0  | 10800 | 1.1091          | 0.8057   | 0.8057 |
  | 0.0996        | 16.0  | 11520 | 1.1134          | 0.8151   | 0.8151 |
  | 0.0837        | 17.0  | 12240 | 1.1288          | 0.8160   | 0.8160 |
  | 0.0711        | 18.0  | 12960 | 1.1499          | 0.8155   | 0.8155 |
  | 0.0594        | 19.0  | 13680 | 1.1622          | 0.8126   | 0.8126 |
  | 0.0569        | 20.0  | 14400 | 1.1661          | 0.8136   | 0.8136 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2