readme: ---
  license: apache-2.0
  datasets:
  - go_emotions
  
  metrics:
  - accuracy
  model-index:
  - name: xtremedistil-emotion
    results:
    - task:
        name: Multi Label Text Classification
        type: multi_label_classification
      dataset:
        name: go_emotions
        type: emotion
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: NaN
        
  ---
  # xtremedistil-l6-h384-go-emotion
  This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the 
  [go_emotions dataset](https://huggingface.co/datasets/go_emotions). 
  
  See notebook for how the model was trained and converted to ONNX format [![Training Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jobergum/emotion/blob/main/TrainGoEmotions.ipynb)
  
  This model is deployed to [aiserv.cloud](https://aiserv.cloud/) for live demo of the model. 
  
  See [https://github.com/jobergum/browser-ml-inference](https://github.com/jobergum/browser-ml-inference) for how to reproduce. 
  
  
  ### Training hyperparameters
  - batch size 128 
  - learning_rate=3e-05
  - epocs 4 
  <pre>
    Num examples = 211225
    Num Epochs = 4
    Instantaneous batch size per device = 128
    Total train batch size (w. parallel, distributed & accumulation) = 128
    Gradient Accumulation steps = 1
    Total optimization steps = 6604
   [6604/6604 53:23, Epoch 4/4]
  Step	Training Loss
  500	0.263200
  1000	0.156900
  1500	0.152500
  2000	0.145400
  2500	0.140500
  3000	0.135900
  3500	0.132800
  4000	0.129400
  4500	0.127200
  5000	0.125700
  5500	0.124400
  6000	0.124100
  6500	0.123400
  </pre>