Add: update model

.ipynb_checkpoints/README-checkpoint.md

@@ -1,1558 +0,0 @@
----
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
-- sentence-embedding
-- mteb
-model-index:
-- name: fea4158624861f232b237be2e106dc7d8295e0da
-  results:
-  - task:
-      type: Clustering
-    dataset:
-      type: lyon-nlp/alloprof
-      name: MTEB AlloProfClusteringP2P
-      config: default
-      split: test
-      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
-    metrics:
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-      value: 56.77190187231352
-    - type: v_measures
-      value: [0.5591529760439443, 0.5974955147482336, 0.5823433887463434, 0.5582030533486634, 0.5608784889863708]
-  - task:
-      type: Clustering
-    dataset:
-      type: lyon-nlp/alloprof
-      name: MTEB AlloProfClusteringS2S
-      config: default
-      split: test
-      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
-    metrics:
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-      value: 43.8035296814567
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-      value: [0.48563274496248593, 0.39879051227524587, 0.4846924607458884, 0.48918774336682036, 0.42626900366019754]
-  - task:
-      type: Reranking
-    dataset:
-      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
-      name: MTEB AlloprofReranking
-      config: default
-      split: test
-      revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
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-      type: lyon-nlp/alloprof
-      name: MTEB AlloprofRetrieval
-      config: default
-      split: test
-      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
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-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification
-      config: default
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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-  - task:
-      type: Retrieval
-    dataset:
-      type: maastrichtlawtech/bsard
-      name: MTEB BSARDRetrieval
-      config: default
-      split: test
-      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
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-  - task:
-      type: Clustering
-    dataset:
-      type: lyon-nlp/clustering-hal-s2s
-      name: MTEB HALClusteringS2S
-      config: default
-      split: test
-      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
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-  - task:
-      type: Clustering
-    dataset:
-      type: reciTAL/mlsum
-      name: MTEB MLSUMClusteringP2P
-      config: default
-      split: test
-      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
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-  - task:
-      type: Clustering
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-      type: reciTAL/mlsum
-      name: MTEB MLSUMClusteringS2S
-      config: default
-      split: test
-      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
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-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification
-      config: default
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
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-      value: 86.88380833072345
-    - type: f1
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-    - type: f1_weighted
-      value: 86.83432508604707
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification
-      config: default
-      split: test
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-      config: default
-      split: test
-      revision: 18193f187b92da67168c655c9973a165ed9593dd
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-      config: default
-      split: test
-      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
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-  - task:
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-      config: default
-      split: test
-      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
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-      split: test
-      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
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-  - task:
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-      split: test
-      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
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-license: apache-2.0
-language:
-    - fr
-    - en
----
-
-# [bilingual-embedding-large](https://huggingface.co/Lajavaness/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](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) 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
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel 
-  (1): Pooling({'word_embedding_dimension': 1024, '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-vn 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
-from pyvi.ViTokenizer import tokenize
-
-sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
-
-model = SentenceTransformer('Lajavaness/bilingual-embedding-large', 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}

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