diff --git "a/.ipynb_checkpoints/README-checkpoint.md" "b/.ipynb_checkpoints/README-checkpoint.md" deleted file mode 100644--- "a/.ipynb_checkpoints/README-checkpoint.md" +++ /dev/null @@ -1,8184 +0,0 @@ ---- -library_name: sentence-transformers -pipeline_tag: sentence-similarity -tags: -- sentence-transformers -- feature-extraction -- sentence-similarity -- transformers -- sentence-embedding -- mteb -model-index: -- name: bilingual-embedding-small - results: - - task: - type: Clustering - dataset: - type: lyon-nlp/alloprof - name: MTEB AlloProfClusteringP2P - config: default - split: test - revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b - metrics: - - type: v_measure - value: 63.19030822769444 - - type: v_measures - value: [0.5938891912573394, 0.6171518411959, 0.6042518292029612, 0.6626602879382325, 0.6471224639325329] - - task: - type: Clustering - dataset: - type: lyon-nlp/alloprof - name: MTEB AlloProfClusteringS2S - config: default - split: test - revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b - 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- fr - - en ---- - -# [bilingual-embedding-small](https://huggingface.co/Lajavaness/bilingual-embedding-small) - -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 [Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384), a pre-trained language model is built upon [multilingual-e5](https://huggingface.co/intfloat/multilingual-e5-small) architecture. The model utilizes MiniLM 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 -``` -SentenceTransformer( - (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel - (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) - (2): Normalize() -) -``` - -## 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/examples/training/data_augmentation/README.html) -- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy. - - -## Usage: - -Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: - -``` -pip install -U sentence-transformers -``` - -Then you can use the model like this: - -```python -from sentence_transformers import SentenceTransformer - -sentences = ["Paris est une capitale de la France", "Paris is a capital of France"] - -model = SentenceTransformer('Lajavaness/bilingual-embedding-small', trust_remote_code=True) -print(embeddings) - -``` - - - - - -## Evaluation - -TODO - -## Citation - - @article{conneau2019unsupervised, - title={Unsupervised cross-lingual representation learning at scale}, - author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, - journal={arXiv preprint arXiv:1911.02116}, - year={2019} - } - - @article{reimers2019sentence, - title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, - author={Nils Reimers, Iryna Gurevych}, - journal={https://arxiv.org/abs/1908.10084}, - year={2019} - } - - @article{thakur2020augmented, - title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks}, - author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna}, - journal={arXiv e-prints}, - pages={arXiv--2010}, - year={2020} \ No newline at end of file