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@@ -19,12 +19,12 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.487
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  ---
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  # Text Classification GoEmotions
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- This a onnx quantized model ahd is fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset using [tasinho/text-classification-goemotions](https://huggingface.co/tasinhoque/text-classification-goemotions) as teacher model.
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  # Load the Model
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@@ -114,10 +114,9 @@ The following hyperparameters were used during training:
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  | Teacher (params) | Student (params) | Set | Score (teacher) | Score (student) |
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  |--------------------|-------------|----------|--------| --------|
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- | tasinhoque/text-classification-goemotions (355M) | MiniLMv2-L6-H384-goemotions-v2 | Validation | 0.514252 |0.484898 |
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- | tasinhoque/text-classification-goemotions (33M) | MiniLMv2-L6-H384-goemotions-v2 (original model) | Test | 0.501937 | 0.486890 |
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- # Training Code, Evaluation & Deployment
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-
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- Check
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.482
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  ---
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  # Text Classification GoEmotions
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+ This a onnx quantized model and is fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset using [tasinho/text-classification-goemotions](https://huggingface.co/tasinhoque/text-classification-goemotions) as teacher model.
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  # Load the Model
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  | Teacher (params) | Student (params) | Set | Score (teacher) | Score (student) |
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  |--------------------|-------------|----------|--------| --------|
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+ | tasinhoque/text-classification-goemotions (355M) | MiniLMv2-L6-H384-goemotions-v2-onnx | Validation | 0.514252 | .0478 |
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+ | tasinhoque/text-classification-goemotions (33M) | MiniLMv2-L6-H384-goemotions-v2-onnx (original model) | Test | 0.501937 | 0.482 |
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+ # Deployment
 
 
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+ Check [this repository](https://github.com/minuva/emotion-prediction-serverless) to see how to easily deploy this model in a serverless environment with fast CPU inference.