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xtremedistil-l6-h384-emotion

This model is a fine-tuned version of 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
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)

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

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
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Text Classification
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Examples
This model can be loaded on the Inference API on-demand.

Dataset used to train bergum/xtremedistil-l6-h384-emotion

Evaluation results