readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - emotion
  metrics:
  - accuracy
  model-index:
  - name: xtremedistil-l6-h384-emotion
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: emotion
        type: emotion
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.928
  ---
  # xtremedistil-l6-h384-emotion
  This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the emotion dataset.
  It achieves the following results on the evaluation set:
  - Accuracy: 0.928
  
  This model can be quantized to int8 and retain accuracy 
  - Accuracy 0.912
  
  <pre>
  import transformers
  import transformers.convert_graph_to_onnx as onnx_convert
  from pathlib import Path
  
  pipeline = transformers.pipeline("text-classification",model=model,tokenizer=tokenizer)
  onnx_convert.convert_pytorch(pipeline, opset=11, output=Path("xtremedistil-l6-h384-emotion.onnx"), use_external_format=False)
  from onnxruntime.quantization import quantize_dynamic, QuantType
  quantize_dynamic("xtremedistil-l6-h384-emotion.onnx", "xtremedistil-l6-h384-emotion-int8.onnx", 
                   weight_type=QuantType.QUInt8)
  </pre>
  
  
  ### Training hyperparameters
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 128
  - eval_batch_size: 8
  - seed: 42
  - num_epochs: 14
  ### Training results
  <pre>
  Epoch	Training Loss	Validation Loss	Accuracy
  1	No log	0.960511	0.689000
  2	No log	0.620671	0.824000
  3	No log	0.435741	0.880000
  4	0.797900	0.341771	0.896000
  5	0.797900	0.294780	0.916000
  6	0.797900	0.250572	0.918000
  7	0.797900	0.232976	0.924000
  8	0.277300	0.216347	0.924000
  9	0.277300	0.202306	0.930500
  10	0.277300	0.192530	0.930000
  11	0.277300	0.192500	0.926500
  12	0.181700	0.187347	0.928500
  13	0.181700	0.185896	0.929500
  14	0.181700	0.185154	0.928000
  </pre>