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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]
<!-- 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
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[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
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### 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
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#### Hardware
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#### Software
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## 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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