
# Model Card: Paraphrase Identification

## Model Details

- **Model Name**: ParaBERT
- **Description**: A fine-tuned paraphrase identification model based on BERT
- **Author**: Lucie Gabagnou, Armand L'Huillier, Yanis Rehoune, Ghiles Idris
- **Language**: Pytorch

## Intended Use

- **Primary intended uses**: This model is designed to identify whether two questions are paraphrases of each other.
- **Primary intended users**: This model is intended for use by NLP researchers and developers who are working on tasks related to paraphrase identification.
- **Out-of-scope use cases**: This model should not be used for tasks outside of paraphrase identification, or in situations where the input data may contain sensitive or confidential information.

## Model Architecture and Training Data

- **Model Architecture**: BERT
- **Training Data**: https://huggingface.co/datasets/bigscience/P3/viewer/glue_qqp_same_thing/train (Only questions)

## Evaluation Data and Results

- **Evaluation Data**: https://huggingface.co/datasets/bigscience/P3/viewer/glue_qqp_same_thing/test
- **Metrics**: Accuracy
- **Results**: 0.95
{"language": ["en"], "datasets": ["bigscience/P3"], "metrics": ["accuracy"], "pipeline_tag": "sentence-similarity"}
transformers pytorch bert text-classification sentence-similarity en dataset:bigscience/P3 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #sentence-similarity #en #dataset-bigscience/P3 #autotrain_compatible #endpoints_compatible #region-us 


# Model Card: Paraphrase Identification

## Model Details

- Model Name: ParaBERT
- Description: A fine-tuned paraphrase identification model based on BERT
- Author: Lucie Gabagnou, Armand L'Huillier, Yanis Rehoune, Ghiles Idris
- Language: Pytorch

## Intended Use

- Primary intended uses: This model is designed to identify whether two questions are paraphrases of each other.
- Primary intended users: This model is intended for use by NLP researchers and developers who are working on tasks related to paraphrase identification.
- Out-of-scope use cases: This model should not be used for tasks outside of paraphrase identification, or in situations where the input data may contain sensitive or confidential information.

## Model Architecture and Training Data

- Model Architecture: BERT
- Training Data: URL (Only questions)

## Evaluation Data and Results

- Evaluation Data: URL
- Metrics: Accuracy
- Results: 0.95
# Model Card: Paraphrase Identification ## Model Details

- Model Name: ParaBERT
- Description: A fine-tuned paraphrase identification model based on BERT
- Author: Lucie Gabagnou, Armand L'Huillier, Yanis Rehoune, Ghiles Idris
- Language: Pytorch ## Intended Use

- Primary intended uses: This model is designed to identify whether two questions are paraphrases of each other.
- Primary intended users: This model is intended for use by NLP researchers and developers who are working on tasks related to paraphrase identification.
- Out-of-scope use cases: This model should not be used for tasks outside of paraphrase identification, or in situations where the input data may contain sensitive or confidential information. ## Model Architecture and Training Data

- Model Architecture: BERT
- Training Data: URL (Only questions) ## Evaluation Data and Results

- Evaluation Data: URL
- Metrics: Accuracy
- Results: 0.95
TAGS
#transformers #pytorch #bert #text-classification #sentence-similarity #en #dataset-bigscience/P3 #autotrain_compatible #endpoints_compatible #region-us 
 # Model Card: Paraphrase Identification ## Model Details

- Model Name: ParaBERT
- Description: A fine-tuned paraphrase identification model based on BERT
- Author: Lucie Gabagnou, Armand L'Huillier, Yanis Rehoune, Ghiles Idris
- Language: Pytorch ## Intended Use

- Primary intended uses: This model is designed to identify whether two questions are paraphrases of each other.
- Primary intended users: This model is intended for use by NLP researchers and developers who are working on tasks related to paraphrase identification.
- Out-of-scope use cases: This model should not be used for tasks outside of paraphrase identification, or in situations where the input data may contain sensitive or confidential information. ## Model Architecture and Training Data

- Model Architecture: BERT
- Training Data: URL (Only questions) ## Evaluation Data and Results

- Evaluation Data: URL
- Metrics: Accuracy
- Results: 0.95
TAGS
#transformers #pytorch #bert #text-classification #sentence-similarity #en #dataset-bigscience/P3 #autotrain_compatible #endpoints_compatible #region-us 
# Model Card: Paraphrase Identification## Model Details

- Model Name: ParaBERT
- Description: A fine-tuned paraphrase identification model based on BERT
- Author: Lucie Gabagnou, Armand L'Huillier, Yanis Rehoune, Ghiles Idris
- Language: Pytorch## Intended Use

- Primary intended uses: This model is designed to identify whether two questions are paraphrases of each other.
- Primary intended users: This model is intended for use by NLP researchers and developers who are working on tasks related to paraphrase identification.
- Out-of-scope use cases: This model should not be used for tasks outside of paraphrase identification, or in situations where the input data may contain sensitive or confidential information.## Model Architecture and Training Data

- Model Architecture: BERT
- Training Data: URL (Only questions)## Evaluation Data and Results

- Evaluation Data: URL
- Metrics: Accuracy
- Results: 0.95