louis-martyr/multi-intent-detection-roBERTa

Model Card for Model ID

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

Model Details

Model Description

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]: FacebookAI/roberta-large

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Uses

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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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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

Epoch Training Loss Validation Loss Accuracy F1 Hamming

1 No log 0.072435 0.000000 0.000000 0.013605

2 No log 0.072522 0.000000 0.000000 0.013605

3 0.092900 0.072396 0.000000 0.000000 0.013605

4 0.092900 0.057199 0.000000 0.008461 0.013592

5 0.065500 0.026986 0.064111 0.316517 0.010247

6 0.065500 0.016471 0.773959 0.928884 0.001825

7 0.021900 0.012533 0.884997 0.961644 0.001097

8 0.021900 0.010155 0.917383 0.969257 0.000868

9 0.009300 0.009068 0.916061 0.967037 0.000935

10 0.009300 0.007922 0.923992 0.969573 0.000854

11 0.009300 0.007272 0.924653 0.970616 0.000818

12 0.005900 0.006749 0.929941 0.971468 0.000805

13 0.005900 0.006336 0.931923 0.972127 0.000773

14 0.004300 0.005852 0.931923 0.973525 0.000746

15 0.004300 0.005644 0.938533 0.974937 0.000697

16 0.003500 0.005535 0.931923 0.972501 0.000773

17 0.003500 0.005492 0.936550 0.974324 0.000737

18 0.003000 0.005351 0.937872 0.974378 0.000733

19 0.003000 0.005338 0.937872 0.975060 0.000719

20 0.002700 0.005275 0.940516 0.975551 0.000697 --> Best model

Results

Fine-tuned metrics:

{

'eval_loss': 0.005275276489555836,

'eval_accuracy': 0.9405155320555189,

'eval_f1': 0.97555142119219,

'eval_hamming': 0.0006969079766738156,

'eval_runtime': 7.2009,

'eval_samples_per_second': 210.114,

'eval_steps_per_second': 1.666,

'epoch': 20.0

}

Summary

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Environmental Impact

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

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