bilingual embedding large EU AI Act Compliance Profile

bilingual-embedding-large

Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of XLM-RoBERTa, a pre-trained language model based on the XLM-RoBERTa architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.

Full Model Architecture

Training and Fine-tuning process

Stage 1: NLI Training

• Dataset: [(SNLI+XNLI) for english+french]
• Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.

Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark

• Dataset: [STSB-fr and en]
• Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library.

Stage 4: Advanced Augmentation Fine-tuning

• Dataset: STSB with generate [silver sample from gold sample](https://www.sbert.net/ex

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