multi-intent-detection-deBERTa

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Multi-Label Classification Model from the Homework#4 in the Natural Language Processing class of Hanyang University.

Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Louis MARTYR
• Model type: Multi-Label Classification
• Language(s) (NLP): English
• Finetuned from model [optional]: microsoft/deberta-base

Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Epoch Training Loss Validation Loss Accuracy F1 Hamming

1 No log 0.071592 0.000000 0.000000 0.013605

2 No log 0.044421 0.003966 0.041837 0.013340

3 0.093600 0.019379 0.296761 0.695941 0.005845

4 0.093600 0.010774 0.805684 0.937958 0.001664

5 0.016600 0.007908 0.858559 0.952991 0.001344

6 0.016600 0.006812 0.867812 0.953844 0.001331

7 0.004000 0.006550 0.869134 0.953207 0.001344

8 0.004000 0.006433 0.874422 0.956688 0.001281

9 0.001900 0.006149 0.872439 0.955389 0.001295

10 0.001900 0.005600 0.888962 0.958396 0.001182

11 0.001900 0.005540 0.885658 0.959592 0.001169 --> Best model

12 0.001100 0.005667 0.884997 0.958190 0.001200

13 0.001100 0.005625 0.879048 0.959149 0.001187

14 0.000800 0.005998 0.881031 0.959557 0.001196

15 0.000800 0.005847 0.882353 0.957629 0.001227

16 0.000600 0.006074 0.877726 0.957484 0.001241

17 0.000600 0.006146 0.875083 0.956739 0.001272

18 0.000500 0.006010 0.884336 0.958790 0.001209

19 0.000500 0.006058 0.881031 0.958078 0.001223

20 0.000400 0.006077 0.881692 0.958275 0.001218

Results

Fine-tuned metrics:

{

'eval_loss': 0.0055404407903552055,

'eval_accuracy': 0.8856576338400529,

'eval_f1': 0.959591960051247,

'eval_hamming': 0.0011690069286141424,

'eval_runtime': 3.3212,

'eval_samples_per_second': 455.556,

'eval_steps_per_second': 3.613,

'epoch': 20.0

}

Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Software

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Citation [optional]

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