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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": true,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,1931 @@
+---
+base_model: almanach/camembert-base
+datasets:
+- CATIE-AQ/frenchSTS
+- CATIE-AQ/frenchNLI
+language: fr
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+pipeline_tag: sentence-similarity
+model-index:
+- name: CATIE-AQ/camembert-base-embedding
+  results:
+  - dataset:
+      config: default
+      name: MTEB AlloProfClusteringP2P (default)
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+      split: test
+      type: lyon-nlp/alloprof
+    metrics:
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+      value: 62.81514840418616
+    - type: v_measure
+      value: 62.81514840418616
+    - type: v_measure_std
+      value: 3.132725810965268
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AlloProfClusteringS2S (default)
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+      split: test
+      type: lyon-nlp/alloprof
+    metrics:
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+    - type: v_measure
+      value: 45.87590134482225
+    - type: v_measure_std
+      value: 3.1798952505373275
+    task:
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+  - dataset:
+      config: default
+      name: MTEB AlloprofReranking (default)
+      revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
+      split: test
+      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
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+    - type: nAUC_mrr_max
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+    - type: nAUC_mrr_std
+      value: 27.15603079445034
+    task:
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+  - dataset:
+      config: default
+      name: MTEB AlloprofRetrieval (default)
+      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
+      split: test
+      type: lyon-nlp/alloprof
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+      type: Retrieval
+  - dataset:
+      config: fr
+      name: MTEB AmazonReviewsClassification (fr)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
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+      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
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+      type: maastrichtlawtech/bsard
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+    - type: nauc_ndcg_at_3_std
+      value: -1.2092966231656004
+    - type: nauc_ndcg_at_5_diff1
+      value: 45.542796430967975
+    - type: nauc_ndcg_at_5_max
+      value: 38.51415439452183
+    - type: nauc_ndcg_at_5_std
+      value: -2.358084841952356
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+      value: -12.950780679125476
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+      value: 21.412917471461256
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+      value: 13.825405119536201
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+      value: -5.272242499865375
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+      value: 31.146070261389795
+    - type: nauc_precision_at_100_std
+      value: 16.76037098641707
+    - type: nauc_precision_at_10_diff1
+      value: 5.022078446921512
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+      value: 32.40126524945171
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+      value: 46.10446435296976
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+      value: 0.6338946522527386
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+      value: 1.8507191498204303
+    - type: nauc_precision_at_20_max
+      value: 32.79240428757858
+    - type: nauc_precision_at_20_std
+      value: 10.475564903497089
+    - type: nauc_precision_at_3_diff1
+      value: 18.485676250880545
+    - type: nauc_precision_at_3_max
+      value: 37.83868390515382
+    - type: nauc_precision_at_3_std
+      value: 0.8764756728424954
+    - type: nauc_precision_at_5_diff1
+      value: 11.175975339969739
+    - type: nauc_precision_at_5_max
+      value: 34.3923017648371
+    - type: nauc_precision_at_5_std
+      value: 1.970006195503709
+    - type: nauc_recall_at_1000_diff1
+      value: 50.00849402651506
+    - type: nauc_recall_at_1000_max
+      value: 76.53252732514734
+    - type: nauc_recall_at_1000_std
+      value: 77.98271681562578
+    - type: nauc_recall_at_100_diff1
+      value: 37.40486283884057
+    - type: nauc_recall_at_100_max
+      value: 53.937687925250145
+    - type: nauc_recall_at_100_std
+      value: 27.02303686880432
+    - type: nauc_recall_at_10_diff1
+      value: 34.16588036777468
+    - type: nauc_recall_at_10_max
+      value: 33.462391339457454
+    - type: nauc_recall_at_10_std
+      value: 0.8457483878677011
+    - type: nauc_recall_at_1_diff1
+      value: 54.11412008072518
+    - type: nauc_recall_at_1_max
+      value: 28.5583920066647
+    - type: nauc_recall_at_1_std
+      value: -2.032440075433671
+    - type: nauc_recall_at_20_diff1
+      value: 35.648128649471246
+    - type: nauc_recall_at_20_max
+      value: 38.25932742254366
+    - type: nauc_recall_at_20_std
+      value: 7.011710372084609
+    - type: nauc_recall_at_3_diff1
+      value: 41.91414480084279
+    - type: nauc_recall_at_3_max
+      value: 31.453812281128368
+    - type: nauc_recall_at_3_std
+      value: -3.674860959584926
+    - type: nauc_recall_at_5_diff1
+      value: 37.937536670026674
+    - type: nauc_recall_at_5_max
+      value: 30.827227889874187
+    - type: nauc_recall_at_5_std
+      value: -5.600317070261898
+    - type: ndcg_at_1
+      value: 48.732
+    - type: ndcg_at_10
+      value: 53.736
+    - type: ndcg_at_100
+      value: 59.65200000000001
+    - type: ndcg_at_1000
+      value: 61.633
+    - type: ndcg_at_20
+      value: 56.313
+    - type: ndcg_at_3
+      value: 48.02
+    - type: ndcg_at_5
+      value: 50.368
+    - type: precision_at_1
+      value: 48.732
+    - type: precision_at_10
+      value: 12.737000000000002
+    - type: precision_at_100
+      value: 1.79
+    - type: precision_at_1000
+      value: 0.20500000000000002
+    - type: precision_at_20
+      value: 7.303
+    - type: precision_at_3
+      value: 28.838
+    - type: precision_at_5
+      value: 21.602
+    - type: recall_at_1
+      value: 30.509999999999998
+    - type: recall_at_10
+      value: 63.23800000000001
+    - type: recall_at_100
+      value: 85.739
+    - type: recall_at_1000
+      value: 98.923
+    - type: recall_at_20
+      value: 71.41999999999999
+    - type: recall_at_3
+      value: 46.208
+    - type: recall_at_5
+      value: 54.913000000000004
+    task:
+      type: Retrieval
+---
+
+
+# CATIE-AQ/camembert-base-embedding
+
+## Description
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [almanach/camembert-base](https://huggingface.co/almanach/camembert-base) (111M parameters). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+Score on the MTEB leaderboard:   
+
+| Model                  | Average | Classification | Clustering | PairClassification | Reranking | Retrieval | STS   | Summarization |
+| ---------------------- | ------- | -------------- | ---------- | ------------------ | --------- | --------- | ----- | ------------- |
+| CATIE-AQ/camembert-base-embedding (111M)      | 60,057  | 66,117         | 45,41      | 79,675             | 71,303    | 45,769    | 82,049 | 30,074       |
+| CATIE-AQ/distilcamembert-base-embedding (68M) | 58,297  | 63,904         | 44,549     | 79,102             | 67,961    | 42,222    | 80,204 | 30,138       |
+
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [almanach/camembert-base](https://huggingface.co/almanach/camembert-base) <!-- at revision bc87ad459e7847ef97658d7db26d402162167ed5 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: CamembertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("CATIE-AQ/camembert-base-embedding")
+# Run inference
+sentences = [
+    "Tenet est sous surveillance depuis novembre, lorsque l'ancien directeur général Jeffrey Barbakow a déclaré que la société a utilisé des prix agressifs pour déclencher des paiements plus élevés pour les patients les plus malades de l'assurance maladie.",
+    "En novembre, Jeffrey Brabakow, le directeur général de l'époque, a déclaré que la société utilisait des prix agressifs pour obtenir des paiements plus élevés pour les patients les plus malades de l'assurance maladie.",
+    'La femme est en route pour un rendez-vous.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+## Citation
+
+### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```

added_tokens.json

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+{
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+}

config.json

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+{
+  "_name_or_path": "almanach/camembert-base",
+  "architectures": [
+    "CamembertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 5,
+  "classifier_dropout": null,
+  "eos_token_id": 6,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "camembert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.40.1",
+    "pytorch": "2.3.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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+oid sha256:2150de93ea34eb5c879c0bad52426754bc69404fd1746fa2ef156741de233b8e
+size 442510176

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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+oid sha256:988bc5a00281c6d210a5d34bd143d0363741a432fefe741bf71e61b1869d4314
+size 810912

special_tokens_map.json

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+{
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+    "<s>NOTUSED",
+    "</s>NOTUSED",
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+  ],
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+  "mask_token": {
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+    "lstrip": true,
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+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>NOTUSED",
+      "lstrip": false,
+      "normalized": false,
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+    "<s>NOTUSED",
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+  "model_max_length": 512,
+  "pad_token": "<pad>",
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+  "tokenizer_class": "CamembertTokenizer",
+  "unk_token": "<unk>"
+}