readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  language: en
  widget:
  - text: "The agent on the phone was very helpful and nice to me."
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-base-uncased-finetuned-surveyclassification
    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. -->
  
  # bert-base-uncased-finetuned-surveyclassification
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a custom survey dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2818
  - Accuracy: 0.9097
  - F1: 0.9097
  
  ## Model description
  
  More information needed
  
  #### Limitations and bias
  
  This model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.
  
  #### How to use
  
  You can use this model with Transformers *pipeline* for Text Classification.
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
  tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
  model = AutoModelForSequenceClassification.from_pretrained("Jorgeutd/bert-base-uncased-finetuned-surveyclassification")
  text_classifier = pipeline("text-classification", model=model,tokenizer=tokenizer, device=0)
  example = "The agent on the phone was very helpful and nice to me."
  results = text_classifier(example)
  print(results)
  ```
  
  ## Training and evaluation data
  
  Custom survey dataset.
  
  ## Training procedure
  SageMaker notebook instance.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-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
  - lr_scheduler_warmup_steps: 100
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.4136        | 1.0   | 902  | 0.2818          | 0.9097   | 0.9097 |
  | 0.2213        | 2.0   | 1804 | 0.2990          | 0.9077   | 0.9077 |
  | 0.1548        | 3.0   | 2706 | 0.3507          | 0.9026   | 0.9026 |
  | 0.1034        | 4.0   | 3608 | 0.4692          | 0.9011   | 0.9011 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.8.1+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  