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library_name: transformers
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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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.
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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## Training Details
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### Training Data
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<!-- This should link to a Dataset 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. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- 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 Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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license: mit
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datasets:
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- sem_eval_2020_task_11
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language:
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- en
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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Given a sentence, our model predicts whether or not the sentence contains "persuasive" language, or language designed to elicit emotions or change
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readers' opinions. The model was tuned on the SemEval 2020 Task 11 dataset. However, we preprocessed the dataset to adapt it from
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multilabel technique classification and span-classification to our binary classification task.
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There are two revisions:
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* BERT - we finetuned `bert-large-cased` on our main branch
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* XLM-RoBERTa - we finetuned `xlm-roberta-base` on our `roberta` branch.
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## Model Details
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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.
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- **Developed by:** Ultraviolet Text
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- **Model type:** BERT / RoBERTa
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- **Language(s) (NLP):** En
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- **License:** MIT
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- **Finetuned from model [optional]:** bert-large-cased / xlm-roberta-base
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## How to Get Started with the Model
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Use the code below to get started with the model.
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### Loading from the main branch (BERT)
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```py
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("bert-large-cased")
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model = AutoModelForSequenceClassification.from_pretrained("chreh/persuasive_language_detector")
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```
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### Loading from the `roberta` branch (XLM RoBERTa)
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```py
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
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model = AutoModelForSequenceClassification.from_pretrained("chreh/persuasive_language_detector", revision="roberta")
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```
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## Training Details
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### Training Data
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<!-- This should link to a Dataset 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. -->
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Training data can be downloaded from [the Semeval website](https://propaganda.qcri.org/semeval2020-task11/).
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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The training was done using Huggingface Trainer on both our local machines and Intel Developer Cloud kernels, enabling us to prototype multiple models simultaneously.
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#### Preprocessing [optional]
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All sentences containing spans of persuasive language techniques were labeled as persuasive language examples, while all others
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were labeled as examples of non-persuasive language.
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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The test data is from the test data of `sem_eval_2020_task_11`, which can be downloaded from [the original website](https://propaganda.qcri.org/semeval2020-task11/).
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The test data contains 38.25% persuasive examples and non-persuasive examples 61.75%. Metrics can be found in the following section
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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Metrics are reported in the format (main_branch), (roberta branch)
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* Accuracy - 0.7165140725669719, 0.7326693227091633
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* Recall - 0.6875584658559402, 0.6822916666666666
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* Precision - 0.5941794664510913, 0.6415279138099902
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* F1 - 0.6374674761491761, 0.6612821807168097
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Overall, the `roberta` branch performs better, and with faster inference times. Thus, we recommend users download from the `roberta` revision.
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