
# 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]

<!-- Provide the basic links for the model. -->

- **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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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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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{}
transformers pytorch tensorboard safetensors bert text-classification arxiv:1910.09700 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 


# 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]



- Repository: URL
- Paper [optional]: 
- Demo [optional]: 

## 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





### Training Procedure 



#### Preprocessing [optional]




#### Training Hyperparameters

- Training regime:  

#### Speeds, Sizes, Times [optional]





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data





#### Factors





#### Metrics





### Results



#### Summary



## Model Examination [optional]





## Environmental Impact



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

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

## Technical Specifications [optional]

### Model Architecture and Objective



### Compute Infrastructure



#### Hardware



#### Software



[optional]



BibTeX:



APA:



## Glossary [optional]





## More Information [optional]



## Model Card Authors [optional]



## Model Card Contact





# 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]



- Repository: URL
- Paper [optional]: 
- Demo [optional]: ## 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 ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact



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

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 
 # 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]



- Repository: URL
- Paper [optional]: 
- Demo [optional]: ## 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 ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters

- Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact



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

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact