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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
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# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("luciegaba/ParaBERT") model = AutoModelForSequenceClassification.from_pretrained("luciegaba/ParaBERT", device_map="auto")