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Model Details
Model Description
This is a specialized cross encoder designed for French language tasks. It is based on Google's BERT (bert-base-multilingual-cased) architecture and fine-tuned on the PhilipMay/stsb_multi_mt French dataset. After 10 epochs of training, the model achieved a Pearson correlation of 0.83621 and a Spearman correlation of 0.82456 on the STS-B test set.
- Developed by: Leviatan Research Team
- Model type: Cross Encoder
- Language(s) (NLP): French
- Finetuned from model [optional]: Google's BERT (bert-base-multilingual-cased)
Results
STS-B Test Set:
- Metric: CECorrelationEvaluator
- Pearson: 0.83621
- Spearman: 0.82456
- Metric: CECorrelationEvaluator
Zero-Shot Test using FQuAD as Knowledge Base:
- Number of questions tested: 3188
- Number of documents considered: 768
- Top 5 k@precision: 0.8563
- Top 5 MRR: 0.6898
Comparison with dangvantuan/CrossEncoder-camembert-large:
- Top 5 k@precision: 0.6688
- Top 5 MRR: 0.4131
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