readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - nlu
  - text-classification
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-base-uncased-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: 0.8903
  datasets:
  - AmazonScience/massive
  language:
  - en
  ---
  
  <!-- 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-uncased-amazon-massive-intent
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on 
  [Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset).
  It achieves the following results on the evaluation set:
  - Loss: 0.4897
  - Accuracy: 0.8903
  - F1: 0.8903
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 2.5862        | 1.0   | 720  | 1.0160          | 0.8096   | 0.8096 |
  | 1.0591        | 2.0   | 1440 | 0.6003          | 0.8716   | 0.8716 |
  | 0.4151        | 3.0   | 2160 | 0.5113          | 0.8859   | 0.8859 |
  | 0.3028        | 4.0   | 2880 | 0.5030          | 0.8883   | 0.8883 |
  | 0.1852        | 5.0   | 3600 | 0.4897          | 0.8903   | 0.8903 |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1