Update README.md

README.md

@@ -6,19 +6,1528 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-- phobert
 - french
+- english
 - sentence-embedding
+- mteb
+model-index:
+- name: 7eff199d41ff669fad99d83cad9249c393c3f14b
+  results:
+  - task:
+      type: Clustering
+    dataset:
+      type: lyon-nlp/alloprof
+      name: MTEB AlloProfClusteringP2P
+      config: default
+      split: test
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+    metrics:
+    - type: v_measure
+      value: 59.69196295449414
+    - type: v_measures
+      value: [0.6355772777559684, 0.4980707615440343, 0.5851538838323186, 0.6567709175938427, 0.5712405288636999]
+  - task:
+      type: Clustering
+    dataset:
+      type: lyon-nlp/alloprof
+      name: MTEB AlloProfClusteringS2S
+      config: default
+      split: test
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+    metrics:
+    - type: v_measure
+      value: 45.607106996926426
+    - type: v_measures
+      value: [0.45846869913649535, 0.42657120373128293, 0.45507356125930876, 0.4258913306353704, 0.4779122207000794]
+  - task:
+      type: Reranking
+    dataset:
+      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
+      name: MTEB AlloprofReranking
+      config: default
+      split: test
+      revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
+    metrics:
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+    - type: nAUC_mrr_diff1
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+    - type: nAUC_mrr_max
+      value: 29.362681962243276
+  - task:
+      type: Retrieval
+    dataset:
+      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 (fr)
+      config: fr
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
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+      value: 39.892
+    - type: f1
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+    - type: f1_weighted
+      value: 38.38126304364462
+  - task:
+      type: Retrieval
+    dataset:
+      type: maastrichtlawtech/bsard
+      name: MTEB BSARDRetrieval
+      config: default
+      split: test
+      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
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+    - type: recall_at_3
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+    - type: recall_at_5
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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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+      value: [0.28558356565178666, 0.2707322246129254, 0.2683693125038299, 0.2703937853835602, 0.22057190525667872]
+  - task:
+      type: Clustering
+    dataset:
+      type: reciTAL/mlsum
+      name: MTEB MLSUMClusteringP2P
+      config: default
+      split: test
+      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
+    metrics:
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+      value: [0.43465029372260205, 0.42821098223656917, 0.43537879149583325, 0.4289578694928627, 0.3794307754465835]
+  - task:
+      type: Clustering
+    dataset:
+      type: reciTAL/mlsum
+      name: MTEB MLSUMClusteringS2S
+      config: default
+      split: test
+      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
+    metrics:
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+      value: [0.4294163918345751, 0.46229994906725164, 0.44188446196569603, 0.43839320352264155, 0.3866853445120933]
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (fr)
+      config: fr 
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
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+      value: 88.33072345756342
+    - type: f1
+      value: 88.11780476022122
+    - type: f1_weighted
+      value: 88.28188145087299
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (fr)
+      config: fr
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 57.854682117131226
+    - type: f1
+      value: 41.121569078191996
+    - type: f1_weighted
+      value: 60.04845437480532
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/masakhanews
+      name: MTEB MasakhaNEWSClassification (fra)
+      config: fra
+      split: test
+      revision: 18193f187b92da67168c655c9973a165ed9593dd
+    metrics:
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+      value: 76.87203791469194
+    - type: f1
+      value: 72.94847557303437
+    - type: f1_weighted
+      value: 76.9128173959562
+  - task:
+      type: Clustering
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClusteringP2P (fra)
+      config: fra
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
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+    - type: v_measures
+      value: [1.0, 0.6446188396257355, 0.28995363026757603, 0.40898735994696084, 0.7224436183265853]
+  - task:
+      type: Clustering
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClusteringS2S (fra)
+      config: fra
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
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+    - type: v_measures
+      value: [1.0, 0.022472587992562534, 0.4686320087689936, 0.811946141094871, 0.7224436183265853]
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (fr)
+      config: fr
+      split: test
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+    metrics:
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+    - type: f1_weighted
+      value: 62.74292948992287
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (fr)
+      config: fr
+      split: test
+      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
+    metrics:
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+    - type: f1_weighted
+      value: 70.35250466465047
+  - task:
+      type: Retrieval
+    dataset:
+      type: jinaai/mintakaqa
+      name: MTEB MintakaRetrieval (fr)
+      config: fr
+      split: test
+      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
+    metrics:
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+    - type: precision_at_3
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+    - type: precision_at_5
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+    - type: recall_at_1
+      value: 19.165
+    - type: recall_at_10
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+    - type: recall_at_1000
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+    - type: recall_at_20
+      value: 58.108000000000004
+    - type: recall_at_3
+      value: 34.644000000000005
+    - type: recall_at_5
+      value: 40.991
+  - task:
+      type: PairClassification
+    dataset:
+      type: GEM/opusparcus
+      name: MTEB OpusparcusPC (fr)
+      config: fr
+      split: test
+      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
+    metrics:
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+        value: 83.51498637602179
+      - type: cos_sim_ap
+        value: 94.18614574224773
+      - type: cos_sim_f1
+        value: 88.3564925730714
+      - type: cos_sim_precision
+        value: 85.37037037037037
+      - type: cos_sim_recall
+        value: 91.55908639523337
+      - type: dot_accuracy
+        value: 83.51498637602179
+      - type: dot_ap
+        value: 94.18614574224773
+      - type: dot_f1
+        value: 88.3564925730714
+      - type: dot_precision
+        value: 85.37037037037037
+      - type: dot_recall
+        value: 91.55908639523337
+      - type: euclidean_accuracy
+        value: 83.51498637602179
+      - type: euclidean_ap
+        value: 94.18614574224773
+      - type: euclidean_f1
+        value: 88.3564925730714
+      - type: euclidean_precision
+        value: 85.37037037037037
+      - type: euclidean_recall
+        value: 91.55908639523337
+      - type: manhattan_accuracy
+        value: 83.51498637602179
+      - type: manhattan_ap
+        value: 94.16717671332795
+      - type: manhattan_f1
+        value: 88.35418671799807
+      - type: manhattan_precision
+        value: 85.71428571428571
+      - type: manhattan_recall
+        value: 91.16186693147964
+      - type: max_accuracy
+        value: 83.51498637602179
+      - type: max_ap
+        value: 94.18614574224773
+      - type: max_f1
+        value: 88.3564925730714
+  - task:
+      type: PairClassification
+    dataset:
+      type: google-research-datasets/paws-x
+      name: MTEB PawsX (fr)
+      config: fr
+      split: test
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+    metrics:
+    - type: cos_sim_accuracy
+      value: 60.699999999999996
+    - type: cos_sim_ap
+      value: 60.20276173325004
+    - type: cos_sim_f1
+      value: 62.716429395921516
+    - type: cos_sim_precision
+      value: 48.05424528301887
+    - type: cos_sim_recall
+      value: 90.2547065337763
+    - type: dot_accuracy
+      value: 60.699999999999996
+    - type: dot_ap
+      value: 60.27996470746299
+    - type: dot_f1
+      value: 62.716429395921516
+    - type: dot_precision
+      value: 48.05424528301887
+    - type: dot_recall
+      value: 90.2547065337763
+    - type: euclidean_accuracy
+      value: 60.699999999999996
+    - type: euclidean_ap
+      value: 60.20276173325004
+    - type: euclidean_f1
+      value: 62.716429395921516
+    - type: euclidean_precision
+      value: 48.05424528301887
+    - type: euclidean_recall
+      value: 90.2547065337763
+    - type: manhattan_accuracy
+      value: 60.699999999999996
+    - type: manhattan_ap
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+    - type: manhattan_f1
+      value: 62.71056661562021
+    - type: manhattan_precision
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+    - type: manhattan_recall
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+    - type: max_accuracy
+      value: 60.699999999999996
+    - type: max_ap
+      value: 60.27996470746299
+    - type: max_f1
+      value: 62.716429395921516
+  - task:
+      type: STS
+    dataset:
+      type: Lajavaness/SICK-fr
+      name: MTEB SICKFr
+      config: default
+      split: test
+      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
+    metrics:
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+      value: 84.24496945719946
+    - type: cos_sim_spearman
+      value: 78.10001513346513
+    - type: euclidean_pearson
+      value: 81.43570951228163
+    - type: euclidean_spearman
+      value: 78.0987784421045
+    - type: manhattan_pearson
+      value: 81.31986646517238
+    - type: manhattan_spearman
+      value: 78.09610194828534
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (fr)
+      config: fr
+      split: test
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+    metrics:
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+      value: 83.07721141521425
+    - type: cos_sim_spearman
+      value: 83.19199466052186
+    - type: euclidean_pearson
+      value: 82.10672022294766
+    - type: euclidean_spearman
+      value: 83.19199466052186
+    - type: manhattan_pearson
+      value: 81.92531847793633
+    - type: manhattan_spearman
+      value: 83.20694689089673
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsb_multi_mt
+      name: MTEB STSBenchmarkMultilingualSTS (fr)
+      config: fr
+      split: test
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+    metrics:
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+    - type: cos_sim_spearman
+      value: 84.40492503459248
+    - type: euclidean_pearson
+      value: 83.8150014101056
+    - type: euclidean_spearman
+      value: 84.40686653864509
+    - type: manhattan_pearson
+      value: 83.6816837321264
+    - type: manhattan_spearman
+      value: 84.2678486368702
+  - task:
+      type: Summarization
+    dataset:
+      type: lyon-nlp/summarization-summeval-fr-p2p
+      name: MTEB SummEvalFr
+      config: default
+      split: test
+      revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
+    metrics:
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+    - type: dot_pearson
+      value: 32.06591974515864
+    - type: dot_spearman
+      value: 30.925383080565222
+  - task:
+      type: Reranking
+    dataset:
+      type: lyon-nlp/mteb-fr-reranking-syntec-s2p
+      name: MTEB SyntecReranking
+      config: default
+      split: test
+      revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
+    metrics:
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+    - type: mrr
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+    - type: nAUC_map_diff1
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+    - type: nAUC_map_max
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+    - type: nAUC_mrr_diff1
+      value: 66.27779227667267
+    - type: nAUC_mrr_max
+      value: 6.651414764738896
+  - task:
+      type: Retrieval
+    dataset:
+      type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
+      name: MTEB SyntecRetrieval
+      config: default
+      split: test
+      revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
+    metrics:
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+      value: 100.0
+    - type: recall_at_3
+      value: 93.0
+    - type: recall_at_5
+      value: 96.0
+  - task:
+      type: Retrieval
+    dataset:
+      type: jinaai/xpqa
+      name: MTEB XPQARetrieval (fr)
+      config: fr
+      split: test
+      revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
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+      value: 71.147
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+      value: 64.183
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+      value: 65.668
+    - type: precision_at_1
+      value: 64.08500000000001
+    - type: precision_at_10
+      value: 15.728
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+      value: 1.9720000000000002
+    - type: precision_at_1000
+      value: 0.207
+    - type: precision_at_20
+      value: 8.705
+    - type: precision_at_3
+      value: 39.03
+    - type: precision_at_5
+      value: 27.717000000000002
+    - type: recall_at_1
+      value: 40.797
+    - type: recall_at_10
+      value: 77.432
+    - type: recall_at_100
+      value: 95.68100000000001
+    - type: recall_at_1000
+      value: 99.666
+    - type: recall_at_20
+      value: 84.773
+    - type: recall_at_3
+      value: 62.083
+    - type: recall_at_5
+      value: 69.786
 license: apache-2.0
 language:
 - fr
 - en
-metrics:
-- pearsonr
-- spearmanr
 ---
 ## Model Description:
-[**french-embedding-LongContext**](https://huggingface.co/dangvantuan/french-embedding-LongContext) is the Embedding Model for French-English language  with context length up to 8096 tokens. This model is a specialized text-embedding trained specifically for the french language, which is built upon [gte-multilingual](Alibaba-NLP/gte-multilingual-base) and trained using the Multi-Negative Ranking Loss, Matryoshka2dLoss and SimilarityLoss.
+[**french-document-embedding**](https://huggingface.co/dangvantuan/french-document-embedding) is an embedding model for documents in the French-English language, with a context length of up to 8096 tokens. This model is a specialized text-embedding model trained specifically for the French-English language. It is built upon [gte-multilingual](Alibaba-NLP/gte-multilingual-base) and trained using the [SimilarityLoss], [Multi-Negative Ranking Loss](https://arxiv.org/abs/1705.00652), [Matryoshka2dLoss](https://arxiv.org/html/2402.14776v1) and [GISTEmbedLoss](https://arxiv.org/abs/2402.16829) using [guide model](https://huggingface.co/Lajavaness/bilingual-embedding-large).  This model embeds and converts long texts or documents into vectors with 786 dimensions, making it useful for vector databases serving semantic search or RAG (Retrieval-Augmented Generation).
 
 ## Full Model Architecture
 ```
@@ -28,14 +1537,6 @@ SentenceTransformer(
   (2): Normalize()
 )
 ```
-## Training and Fine-tuning process
-The model underwent a rigorous four-stage training and fine-tuning process, each tailored to enhance its ability to generate precise and contextually relevant sentence embeddings for the french language. Below is an outline of these stages:
-#### Stage 1: Training NLI on dataset XNLI: 
-- Dataset: XNLI (fr-en)
-- Method: Training using Multi-Negative Ranking Loss and Matryoshka2dLoss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.
-### Stage 2: Fine-tuning for Semantic Textual Similarity on STS Benchmark
-- Dataset: STS-B (fr-en)
-- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. This stage honed the model's precision in capturing semantic similarity across various types of french texts.
 
 
 ## Usage:
@@ -54,7 +1555,7 @@ sentences = ["Paris est une capitale de la France", "Paris is a capital of Franc
 
 
 
-model = SentenceTransformer('dangvantuan/french-embedding-LongContext', trust_remote_code=True)
+model = SentenceTransformer('dangvantuan/french-document-embedding', trust_remote_code=True)
 embeddings = model.encode(sentences)
 print(embeddings)
 
@@ -78,7 +1579,6 @@ print(embeddings)
 	   year={2019}
 	}
 
-
     @article{zhang2024mgte,
       title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
       author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
@@ -98,4 +1598,22 @@ print(embeddings)
       author={Li, Xianming and Li, Zongxi and Li, Jing and Xie, Haoran and Li, Qing},
       journal={arXiv preprint arXiv:2402.14776},
       year={2024}
+    }
+    
+    @misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+    }
+
+    @misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
     }