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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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 +1,1932 @@
+---
+base_model: cmarkea/distilcamembert-base
+datasets:
+- CATIE-AQ/frenchSTS
+- CATIE-AQ/frenchNLI
+language: fr
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+model-index:
+- name: CATIE-AQ/distilcamembert-base-embedding
+  results:
+  - dataset:
+      config: default
+      name: MTEB AlloProfClusteringP2P (default)
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+      split: test
+      type: lyon-nlp/alloprof
+    metrics:
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+      value: 59.596627257424096
+    - type: v_measure
+      value: 59.596627257424096
+    - type: v_measure_std
+      value: 4.010558670407883
+    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: 47.06111698347941
+    - type: v_measure_std
+      value: 1.5225244073165662
+    task:
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+      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_std
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+    task:
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+  - dataset:
+      config: default
+      name: MTEB AlloprofRetrieval (default)
+      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
+      split: test
+      type: lyon-nlp/alloprof
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+    task:
+      type: Retrieval
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+      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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+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
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+      type: google-research-datasets/paws-x
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+      value: 3.775640202932283
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+      value: 34.78244550057632
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+      value: 10.477583248238298
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+      value: 20.840731694529214
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+      value: 39.75761979107115
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+      value: 38.18917028855593
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+      value: 52.771811958456404
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+      value: 29.761953273353463
+    - type: nauc_recall_at_1_std
+      value: -3.3283748548746397
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+      value: 35.30332696049941
+    - type: nauc_recall_at_20_max
+      value: 38.8152320021852
+    - type: nauc_recall_at_20_std
+      value: 5.554654053834975
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+      value: 43.74124312901872
+    - type: nauc_recall_at_3_max
+      value: 33.409247871088986
+    - type: nauc_recall_at_3_std
+      value: -2.3732020261032707
+    - type: nauc_recall_at_5_diff1
+      value: 41.29196881052914
+    - type: nauc_recall_at_5_max
+      value: 37.29462078993601
+    - type: nauc_recall_at_5_std
+      value: 2.2929109906730765
+    - type: ndcg_at_1
+      value: 46.595
+    - type: ndcg_at_10
+      value: 51.273
+    - type: ndcg_at_100
+      value: 57.379999999999995
+    - type: ndcg_at_1000
+      value: 59.602
+    - type: ndcg_at_20
+      value: 53.931
+    - type: ndcg_at_3
+      value: 46.678
+    - type: ndcg_at_5
+      value: 47.865
+    - type: precision_at_1
+      value: 46.595
+    - type: precision_at_10
+      value: 12.136
+    - type: precision_at_100
+      value: 1.73
+    - type: precision_at_1000
+      value: 0.20400000000000001
+    - type: precision_at_20
+      value: 7.016
+    - type: precision_at_3
+      value: 28.438000000000002
+    - type: precision_at_5
+      value: 20.401
+    - type: recall_at_1
+      value: 29.424
+    - type: recall_at_10
+      value: 59.666
+    - type: recall_at_100
+      value: 83.22
+    - type: recall_at_1000
+      value: 98.151
+    - type: recall_at_20
+      value: 68.195
+    - type: recall_at_3
+      value: 45.189
+    - type: recall_at_5
+      value: 51.32
+    task:
+      type: Retrieval
+---
+
+
+# CATIE-AQ/distilcamembert-base-embedding
+
+## Description
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) (68.1M 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:** [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) <!-- at revision 82fb7671777bb680416031dcced25c7bfa6e859b -->
+- **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/distilcamembert-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",
+}
+```

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "cmarkea/distilcamembert-base",
+  "architectures": [
+    "CamembertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "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": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "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": "4.42.3",
+    "pytorch": "2.3.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96df34ffccd7ccc030e94eb019dc90f6b30b97a24b73f82c3118d3c2820ab4a2
+size 272390400

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:988bc5a00281c6d210a5d34bd143d0363741a432fefe741bf71e61b1869d4314
+size 810912

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<s>NOTUSED",
+    "</s>NOTUSED"
+  ],
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "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,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>NOTUSED",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "32004": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<s>NOTUSED",
+    "</s>NOTUSED"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "CamembertTokenizer",
+  "unk_token": "<unk>"
+}