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# Model Card for distilbert-base-task-multi-label-classification Model

## Model Details

### Model Description

This model is based on the distillation of the BERT base model, which is a widely used language model. 
The distillation process involves training a smaller model to mimic the behavior and predictions of the larger BERT model. 
The purpose of this model is to perform fine-tuning on the distilbert-base-pwc-task-multi-label-classification checkpoint for multi-label classification tasks.

Fine-tuning approach can be applied to other models such as RoBERTa, DeBERTa, DistilBERT, CANINE, and more. The notebook provides a practical guide for utilizing these models in various classification scenarios. 


- **Developed by:** Lina Saba
- **Model type:** bert for multi-label classification
- **Language(s) (NLP):** Python
- **Finetuned from model:** distilbert-base-pwc-task-multi-label-classification

### Model Sources [optional]

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- **Repository:** https://colab.research.google.com/drive/1Z314gK2qixK_0ujgQ3nvqvar1iV3QnoF?usp=sharing
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

This model aims to fine-tune BERT to predict one or more labels for a given piece of text. 
The related notebook illustrates how to fine-tune a distilbert-base-pwc-task-multi-label-classification model, Knowing that it's the same way to fine-tune a RoBERTa, DeBERTa, DistilBERT, CANINE, ... checkpoint.

### Direct Use

Predict the labels of a piece of text from this 
list = { 
         0: 'aspersion',
         1: 'hyperbole',
         2: 'lying',
         3: 'namecalling',
         4: 'noncooperation',
         5: 'offtopic',
         6: 'other_incivility',
         7: 'pejorative',
         8: 'sarcasm',
         9: 'vulgarity'
       }

### Downstream Use [optional]

This model is fine-tuned on a dataset; a collection of more than 6000 comments on Arizona Daily Star news articles 
from 2011 that have been manually annotated for various forms of incivility including aspersion, namecalling, sarcasm, and vulgarity.

## Bias, Risks, and Limitations

Technical limitations : 
        - Can't print more than one identified label using pipeline. 
        - Half of the test results aren't exactly the same as what expected

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### 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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### Results

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#### 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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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