---
library_name: transformers
language:
- en
metrics:
- accuracy
- f1
base_model:
- FacebookAI/roberta-large
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
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]:** FacebookAI/roberta-large

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below 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	0.072435	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



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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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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## Glossary [optional]

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