End of training

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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
+}

README.md

@@ -0,0 +1,690 @@
+---
+base_model: intfloat/multilingual-e5-base
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- cosine_mcc
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:2467
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: 'Date de début: non précisée
+
+    Date de fin (clôture): non précisée
+
+    Date de début de la future campagne: non précisée'
+  sentences:
+  - '''Aménageurs privés'':entité|INTERVIENT_POUR|''Établissements publics territoriaux
+    franciliens'':entité'
+  - '''Commission permanente du Conseil régional'':groupe|DÉSIGNE|''Projets retenus'':__inferred__'
+  - '''Date de fin'':concept|EST|''non précisée'':__inferred__'
+- source_sentence: 'Procédures et démarches: Deux
+
+    appels à projets sont lancés chaque année.  Le candidat doit prendre contact avec
+    la direction de
+
+    l’aménagement durable du territoire avant la date de dépôt afin de préciser
+
+    son projet et de s’assurer de son éligibilité (via votre interlocuteur habituel
+
+    ou [email protected]). Le dossier de candidature est à remplir sur mesdemarches.iledefrance.fr.   Un
+
+    jury d’élus et de personnalités qualifiées se réunit pour examiner les dossiers
+
+    et proposer des lauréats. L''attribution définitive des aides est votée en
+
+    commission permanente.  Ce
+
+    dispositif d’aide peut être cumulable avec le Fonds Vert mis en place par l’Etat.
+
+    Les conditions d’éligibilité et d’intervention propres à chacun des dispositifs
+
+    ainsi que les contacts et liens utiles sont présentés dans le document "Tableau
+    AAP Friches 2023" en annexe de cette page.
+
+    Bénéficiaires: Collectivité ou institution - Autre (GIP, copropriété, EPA...),
+    Collectivité ou institution - Communes de 10 000 à 20 000 hab, Collectivité ou
+    institution - Communes de 2000 à 10 000 hab, Collectivité ou institution - Communes
+    de < 2000 hab, Collectivité ou institution - Communes de > 20 000 hab, Collectivité
+    ou institution - Département, Collectivité ou institution - EPT / Métropole du
+    Grand Paris, Collectivité ou institution - EPCI'
+  sentences:
+  - '''Fonds Vert'':programme|MIS_EN_PLACE_PAR|''Etat'':organisation'
+  - '''démonstration et initiation sportive'':activité|ENCADRÉ_PAR|''Ambassadrice
+    et Ambassadeur du Sport'':personne'
+  - '''Association'':entité|EST|''Bénéficiaires'':__inferred__'
+- source_sentence: 'Procédures et démarches: Dépôt du dossier de candidature sur la
+    plateforme des aides régionales (mesdemarches.iledefrance.fr).
+
+    Bénéficiaires: Collectivité ou institution - Communes de < 2000 hab, Collectivité
+    ou institution - Communes de 2000 à 10 000 hab, Collectivité ou institution -
+    Communes de 10 000 à 20 000 hab, Collectivité ou institution - Communes de > 20
+    000 hab, Collectivité ou institution - EPCI, Collectivité ou institution - EPT
+    / Métropole du Grand Paris, Collectivité ou institution - Département, Collectivité
+    ou institution - Bailleurs sociaux, Collectivité ou institution - Autre (GIP,
+    copropriété, EPA...)
+
+    Précision sure les bénéficiaires: Toutes les structures de droit public ou de
+    droit privé'
+  sentences:
+  - '''mesdemarches.iledefrance.fr'':plateforme|ACCEPTE_DEMANDE|''Collectivité ou
+    institution - Communes de 10 000 à 20 000 hab'':organisation'
+  - '''plateforme des aides régionales'':plateforme|CIBLE|''Collectivité ou institution
+    - EPT / Métropole du Grand Paris'':organisation'
+  - '''projets éligibles'':projet|AMÉLIORE_CONDITIONS_VIE|''résidents'':personne'
+- source_sentence: 'Procédures et démarches: Les demandes d’aide devront être déposées
+    sur mesdemarches.iledefrance.fr, la plateforme des aides régionales.
+
+    Bénéficiaires: Particulier - Francilien, Professionnel - Culture, Professionnel
+    - Patrimoine, Association - Fondation, Association - ONG, Association - Régie
+    par la loi de 1901, Collectivité ou institution - Autre (GIP, copropriété, EPA...),
+    Collectivité ou institution - Bailleurs sociaux, Collectivité ou institution -
+    Communes de 10 000 à 20 000 hab, Collectivité ou institution - Communes de 2000
+    à 10 000 hab, Collectivité ou institution - Communes de < 2000 hab, Collectivité
+    ou institution - Communes de > 20 000 hab, Collectivité ou institution - Département,
+    Collectivité ou institution - EPCI, Collectivité ou institution - EPT / Métropole
+    du Grand Paris
+
+    Précision sure les bénéficiaires: Sont éligibles les propriétaires publics et
+    privés de maisons ou d’ateliers d’artistes.Les aménageurs mandatés par les collectivités
+    territoriales peuvent être bénéficiaires. Une convention de délégation de maîtrise
+    d’ouvrage doit avoir été signée entre la collectivité et l’aménageur.L’établissement
+    doit avoir fait l’objet d’un projet culturel et bénéficier d’une expertise scientifique.
+    La présence, le témoignage ou la trace tangibles de l’artiste ayant vécu sur place
+    doivent être attestés.Les établissements bénéficiant du label délivré par la DRAC
+    « Maisons des illustres » sont également concernés par le dispositif.'
+  sentences:
+  - '''mesdemarches.iledefrance.fr'':plateforme|ACCEPTE_DOSSIERS|''Collectivité ou
+    institution - Communes de 10 000 à 20 000 hab'':organisation'
+  - '''mesdemarches.iledefrance.fr'':plateforme|ACCEPTE_DEMANDE|''établissements avec
+    projet culturel et expertise scientifique'':bénéficiaire'
+  - '''plateforme des aides régionales'':plateforme|CIBLE|''Collectivité ou institution
+    - Communes de 2000 à 10 000 hab'':organisation'
+- source_sentence: 'Procédures et démarches: Déposez sur mesdemarches.iledefrance.fr votre dossier
+    de demande de subvention présentant le projet de manière précise et comportant
+    toutes les pièces permettant l’instruction du dossier, réputé complet, par les
+    services de la Région. Après examen du dossier, la demande de subvention sera
+    soumise à la Commission permanente régionale pour délibération.  Le versement
+    de la subvention est subordonné à la signature préalable d’une convention.
+
+    Bénéficiaires: Collectivité ou institution - Communes de 10 000 à 20 000 hab,
+    Collectivité ou institution - Communes de 2000 à 10 000 hab, Collectivité ou institution
+    - Communes de < 2000 hab, Collectivité ou institution - Communes de > 20 000 hab,
+    Collectivité ou institution - EPCI, Collectivité ou institution - EPT / Métropole
+    du Grand Paris
+
+    Précision sure les bénéficiaires: Pour les PEMR et aires de covoiturage : État,
+    Départements, EPCI, Communes, Syndicats mixtes,Ville de Paris.Pour les voies réservées :
+    État, Départements, EPCI.'
+  sentences:
+  - '''Date de début de la future campagne'':concept|EST|''non précisée'':__inferred__'
+  - '''prêt d''amorçage'':aide|FINANCE|''besoin en fonds de roulement'':concept'
+  - '''subvention'':__inferred__|SUBORDONNÉ_À|''convention'':document'
+model-index:
+- name: SentenceTransformer based on intfloat/multilingual-e5-base
+  results:
+  - task:
+      type: binary-classification
+      name: Binary Classification
+    dataset:
+      name: BinaryClassifEval
+      type: BinaryClassifEval
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9983766233766234
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: -0.09276729822158813
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.9991876523151909
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: -0.09276729822158813
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 1.0
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9983766233766234
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.9999999999999999
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 0.0
+      name: Cosine Mcc
+---
+
+# SentenceTransformer based on intfloat/multilingual-e5-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the json dataset. 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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()
+)
+```
+
+## 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("Lettria/grag-go-idf-mult_neg_rk_10-trial-3")
+# Run inference
+sentences = [
+    'Procédures et démarches: Déposez sur\xa0mesdemarches.iledefrance.fr\xa0votre\xa0dossier de demande de subvention présentant le projet de manière précise et comportant toutes les pièces permettant l’instruction du dossier, réputé complet, par les services de la Région. Après examen du dossier, la demande de subvention sera soumise à la Commission permanente régionale pour délibération.  Le versement de la subvention est subordonné à la signature préalable d’une convention.\nBénéficiaires: Collectivité ou institution - Communes de 10 000 à 20 000 hab, Collectivité ou institution - Communes de 2000 à 10 000 hab, Collectivité ou institution - Communes de < 2000 hab, Collectivité ou institution - Communes de > 20 000 hab, Collectivité ou institution - EPCI, Collectivité ou institution - EPT / Métropole du Grand Paris\nPrécision sure les bénéficiaires: Pour les PEMR et aires de covoiturage : État, Départements, EPCI, Communes, Syndicats mixtes,Ville de Paris.Pour les voies réservées\xa0: État, Départements, EPCI.',
+    "'subvention':__inferred__|SUBORDONNÉ_À|'convention':document",
+    "'Date de début de la future campagne':concept|EST|'non précisée':__inferred__",
+]
+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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Binary Classification
+
+* Dataset: `BinaryClassifEval`
+* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
+
+| Metric                    | Value   |
+|:--------------------------|:--------|
+| cosine_accuracy           | 0.9984  |
+| cosine_accuracy_threshold | -0.0928 |
+| cosine_f1                 | 0.9992  |
+| cosine_f1_threshold       | -0.0928 |
+| cosine_precision          | 1.0     |
+| cosine_recall             | 0.9984  |
+| **cosine_ap**             | **1.0** |
+| cosine_mcc                | 0.0     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### json
+
+* Dataset: json
+* Size: 2,467 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                            | sentence2                                                                         | label                        |
+  |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                               | string                                                                            | int                          |
+  | details | <ul><li>min: 26 tokens</li><li>mean: 191.64 tokens</li><li>max: 429 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 31.2 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | sentence2                                                                                                         | label          |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>Type de project: L’excès de précipitations tout au long de l’année a conduit à une chute spectaculaire des rendements des céréales d’été et des protéagineux (blé, orge, pois, féverole, etc.) que produisent 90% des agriculteurs d’Île-de-France, historique grenier à blé du pays.   Tributaires naturels du fleurissement des cultures, les apiculteurs professionnels de la région ont également souffert de ces dérèglements climatiques.La Région accompagne les exploitations concernées en leur apportant une aide exceptionnelle.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | <code>'excès de précipitations':phénomène|DIMINUE|'rendements des protéagineux':concept</code>                    | <code>1</code> |
+  | <code>Type de project: Dans le cadre de sa stratégie « Impact 2028 », la Région s’engage dans la défense de la souveraineté industrielle en renforçant son soutien à une industrie circulaire et décarbonée, porteuse d’innovations et créatrice d’emplois.  PM'up Jeunes pousses industrielles soutient les projets d’implantation d’une première usine tournée vers la décarbonation, l’efficacité énergétique et la circularité des processus de production.  Ces projets peuvent prendre l'une de ces formes : Une première unité de production industrielle, après une phase de prototypage,Une ligne pilote de production industrielle, en interne ou chez un tiers situé en Île-de-France, à condition que sa production soit destinée à de premières commercialisations,La transformation d’une unité de production pilote à une unité de production industrielle</code>                                                                                                                                                                            | <code>'Région Île-de-France':organisation|soutient|'industrie décarbonée':concept</code>                          | <code>1</code> |
+  | <code>Procédures et démarches: Le dépôt des demandes de subvention se fait en ligne sur la plateforme régionale mesdemarches.iledefrance.fr : Session de dépôt unique pour les nouvelles demandes : du 30 septembre au 4 novembre 2024 (11 heures) pour des festivals qui se déroulent entre le 1er mars 2025 et le 28 février 2026 (vote à la CP de mars 2025).  Pour les demandes de renouvellement, un mail est envoyé aux structures concernées par le service du Spectacle vivant en amont de chaque session de dépôt.<br>Bénéficiaires: Professionnel - Culture, Association - Fondation, Association - Régie par la loi de 1901, Association - ONG, Collectivité ou institution - Communes de 10 000 à 20 000 hab, Collectivité ou institution - Autre (GIP, copropriété, EPA...), Collectivité ou institution - Communes de 2000 à 10 000 hab, Collectivité ou institution - Communes de < 2000 hab, Collectivité ou institution - Communes de > 20 000 hab, Collectivité ou institution - Département, Collectivité ou institution - EPC...</code> | <code>'Collectivité ou institution - EPCI':bénéficiaire|PEUT_BÉNÉFICIER|'demandes de subvention':procédure</code> | <code>1</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### json
+
+* Dataset: json
+* Size: 616 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 616 samples:
+  |         | sentence1                                                                            | sentence2                                                                          | label                        |
+  |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                               | string                                                                             | int                          |
+  | details | <ul><li>min: 24 tokens</li><li>mean: 188.12 tokens</li><li>max: 394 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 31.2 tokens</li><li>max: 133 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | sentence2                                                                        | label          |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------|
+  | <code>Type de project: Le programme propose des rencontres le samedi après-midi dans une université ou une grande école réputée, entre les professionnels bénévoles et les lycéens et collégiens sous la forme d'atelier thématiques.  Ces moments de rencontre touchent à une grande multitude de domaines d’activités.  L'objectif est de donner l’opportunité aux jeunes les plus enclavés d’échanger avec des intervenants professionnels aux parcours atypiques et inspirants. Les intervenants suscitent les ambitions et élargissent les perspectives des élèves.</code> | <code>'rencontres':événement|impliquent|'professionnels bénévoles':groupe</code> | <code>1</code> |
+  | <code>Précision sure les bénéficiaires: Communes,Établissements publics de coopération intercommunale (avec ou sans fiscalité propre),Établissements publics territoriaux franciliens,Départements,Aménageurs publics et privés (lorsque ces derniers interviennent à la demande ou pour le compte d'une collectivité précitée).</code>                                                                                                                                                                                                                                         | <code>'Aménageurs privés':entité|INTERVIENT_POUR|'Départements':entité</code>    | <code>1</code> |
+  | <code>Date de début: non précisée<br>Date de fin (clôture): non précisée<br>Date de début de la future campagne: non précisée</code>                                                                                                                                                                                                                                                                                                                                                                                                                                            | <code>'Date de fin':concept|EST|'non précisée':__inferred__</code>               | <code>1</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 2.044202693407718e-05
+- `num_train_epochs`: 20
+- `lr_scheduler_type`: cosine
+- `warmup_steps`: 124
+- `bf16`: True
+- `tf32`: True
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `hub_model_id`: Lettria/grag-go-idf-mult_neg_rk_10-trial-3
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 4
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2.044202693407718e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 20
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 124
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: True
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: Lettria/grag-go-idf-mult_neg_rk_10-trial-3
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch    | Step     | Training Loss | Validation Loss | BinaryClassifEval_cosine_ap |
+|:--------:|:--------:|:-------------:|:---------------:|:---------------------------:|
+| 0.1621   | 50       | 1.4756        | -               | -                           |
+| 0.3241   | 100      | 0.6024        | -               | -                           |
+| 0.4862   | 150      | 0.5528        | -               | -                           |
+| 0.6483   | 200      | 0.3826        | -               | -                           |
+| 0.8104   | 250      | 0.3344        | -               | -                           |
+| 0.9724   | 300      | 0.355         | -               | -                           |
+| 1.0      | 309      | -             | 0.1723          | 1.0                         |
+| 1.1329   | 350      | 0.2415        | -               | -                           |
+| 1.2950   | 400      | 0.1983        | -               | -                           |
+| 1.4571   | 450      | 0.2042        | -               | -                           |
+| 1.6191   | 500      | 0.1614        | -               | -                           |
+| 1.7812   | 550      | 0.245         | -               | -                           |
+| 1.9433   | 600      | 0.1246        | -               | -                           |
+| 2.0      | 618      | -             | 0.1204          | 1.0                         |
+| 2.1037   | 650      | 0.1493        | -               | -                           |
+| 2.2658   | 700      | 0.1097        | -               | -                           |
+| 2.4279   | 750      | 0.0856        | -               | -                           |
+| 2.5900   | 800      | 0.0781        | -               | -                           |
+| 2.7520   | 850      | 0.1151        | -               | -                           |
+| 2.9141   | 900      | 0.1528        | -               | -                           |
+| 3.0      | 927      | -             | 0.1297          | 1.0                         |
+| 3.0746   | 950      | 0.0552        | -               | -                           |
+| 3.2366   | 1000     | 0.0563        | -               | -                           |
+| 3.3987   | 1050     | 0.0625        | -               | -                           |
+| 3.5608   | 1100     | 0.0516        | -               | -                           |
+| 3.7229   | 1150     | 0.0674        | -               | -                           |
+| 3.8849   | 1200     | 0.129         | -               | -                           |
+| 4.0      | 1236     | -             | 0.1648          | 1.0                         |
+| 4.0454   | 1250     | 0.0445        | -               | -                           |
+| 4.2075   | 1300     | 0.0603        | -               | -                           |
+| 4.3695   | 1350     | 0.0874        | -               | -                           |
+| 4.5316   | 1400     | 0.0353        | -               | -                           |
+| 4.6937   | 1450     | 0.064         | -               | -                           |
+| 4.8558   | 1500     | 0.0612        | -               | -                           |
+| 5.0      | 1545     | -             | 0.2055          | 1.0                         |
+| 5.0162   | 1550     | 0.0554        | -               | -                           |
+| 5.1783   | 1600     | 0.0319        | -               | -                           |
+| 5.3404   | 1650     | 0.0698        | -               | -                           |
+| 5.5024   | 1700     | 0.0651        | -               | -                           |
+| 5.6645   | 1750     | 0.0555        | -               | -                           |
+| 5.8266   | 1800     | 0.122         | -               | -                           |
+| 5.9887   | 1850     | 0.0266        | -               | -                           |
+| 6.0      | 1854     | -             | 0.1933          | 1.0                         |
+| 6.1491   | 1900     | 0.0636        | -               | -                           |
+| 6.3112   | 1950     | 0.0158        | -               | -                           |
+| 6.4733   | 2000     | 0.0156        | -               | -                           |
+| 6.6353   | 2050     | 0.0445        | -               | -                           |
+| 6.7974   | 2100     | 0.071         | -               | -                           |
+| 6.9595   | 2150     | 0.0318        | -               | -                           |
+| 7.0      | 2163     | -             | 0.1893          | 1.0                         |
+| 7.1199   | 2200     | 0.046         | -               | -                           |
+| 7.2820   | 2250     | 0.0353        | -               | -                           |
+| 7.4441   | 2300     | 0.071         | -               | -                           |
+| 7.6062   | 2350     | 0.0373        | -               | -                           |
+| 7.7682   | 2400     | 0.0784        | -               | -                           |
+| 7.9303   | 2450     | 0.0684        | -               | -                           |
+| 8.0      | 2472     | -             | 0.1226          | 1.0                         |
+| 8.0908   | 2500     | 0.0573        | -               | -                           |
+| 8.2528   | 2550     | 0.0146        | -               | -                           |
+| 8.4149   | 2600     | 0.0208        | -               | -                           |
+| 8.5770   | 2650     | 0.0143        | -               | -                           |
+| 8.7391   | 2700     | 0.0779        | -               | -                           |
+| 8.9011   | 2750     | 0.0312        | -               | -                           |
+| 9.0      | 2781     | -             | 0.1612          | 1.0                         |
+| 9.0616   | 2800     | 0.034         | -               | -                           |
+| 9.2237   | 2850     | 0.0163        | -               | -                           |
+| 9.3857   | 2900     | 0.046         | -               | -                           |
+| 9.5478   | 2950     | 0.0745        | -               | -                           |
+| 9.7099   | 3000     | 0.0313        | -               | -                           |
+| 9.8720   | 3050     | 0.0238        | -               | -                           |
+| 10.0     | 3090     | -             | 0.1342          | 1.0                         |
+| 10.0324  | 3100     | 0.028         | -               | -                           |
+| 10.1945  | 3150     | 0.0084        | -               | -                           |
+| 10.3566  | 3200     | 0.051         | -               | -                           |
+| 10.5186  | 3250     | 0.0118        | -               | -                           |
+| 10.6807  | 3300     | 0.032         | -               | -                           |
+| 10.8428  | 3350     | 0.0679        | -               | -                           |
+| 11.0     | 3399     | -             | 0.1355          | 1.0                         |
+| 11.0032  | 3400     | 0.0084        | -               | -                           |
+| 11.1653  | 3450     | 0.0112        | -               | -                           |
+| 11.3274  | 3500     | 0.0228        | -               | -                           |
+| 11.4895  | 3550     | 0.0119        | -               | -                           |
+| 11.6515  | 3600     | 0.0511        | -               | -                           |
+| 11.8136  | 3650     | 0.0363        | -               | -                           |
+| 11.9757  | 3700     | 0.0161        | -               | -                           |
+| 12.0     | 3708     | -             | 0.1345          | 1.0                         |
+| 12.1361  | 3750     | 0.0054        | -               | -                           |
+| 12.2982  | 3800     | 0.0142        | -               | -                           |
+| 12.4603  | 3850     | 0.0045        | -               | -                           |
+| 12.6224  | 3900     | 0.0272        | -               | -                           |
+| 12.7844  | 3950     | 0.0064        | -               | -                           |
+| 12.9465  | 4000     | 0.023         | -               | -                           |
+| **13.0** | **4017** | **-**         | **0.1177**      | **1.0**                     |
+| 13.1070  | 4050     | 0.0234        | -               | -                           |
+| 13.2690  | 4100     | 0.0067        | -               | -                           |
+| 13.4311  | 4150     | 0.019         | -               | -                           |
+| 13.5932  | 4200     | 0.0051        | -               | -                           |
+| 13.7553  | 4250     | 0.0117        | -               | -                           |
+| 13.9173  | 4300     | 0.0244        | -               | -                           |
+| 14.0     | 4326     | -             | 0.1225          | 1.0                         |
+| 14.0778  | 4350     | 0.0268        | -               | -                           |
+| 14.2399  | 4400     | 0.0041        | -               | -                           |
+| 14.4019  | 4450     | 0.0165        | -               | -                           |
+| 14.5640  | 4500     | 0.0028        | -               | -                           |
+| 14.7261  | 4550     | 0.0156        | -               | -                           |
+| 14.8882  | 4600     | 0.007         | -               | -                           |
+| 15.0     | 4635     | -             | 0.1199          | 1.0000                      |
+| 15.0486  | 4650     | 0.0178        | -               | -                           |
+| 15.2107  | 4700     | 0.004         | -               | -                           |
+| 15.3728  | 4750     | 0.0063        | -               | -                           |
+| 15.5348  | 4800     | 0.0161        | -               | -                           |
+| 15.6969  | 4850     | 0.0119        | -               | -                           |
+| 15.8590  | 4900     | 0.0138        | -               | -                           |
+| 16.0     | 4944     | -             | 0.1232          | 1.0                         |
+| 16.0194  | 4950     | 0.0154        | -               | -                           |
+| 16.1815  | 5000     | 0.0201        | -               | -                           |
+| 16.3436  | 5050     | 0.0135        | -               | -                           |
+| 16.5057  | 5100     | 0.0285        | -               | -                           |
+| 16.6677  | 5150     | 0.0395        | -               | -                           |
+| 16.8298  | 5200     | 0.0011        | -               | -                           |
+| 16.9919  | 5250     | 0.0104        | -               | -                           |
+| 17.0     | 5253     | -             | 0.1274          | 1.0                         |
+| 17.1524  | 5300     | 0.0158        | -               | -                           |
+| 17.3144  | 5350     | 0.0502        | -               | -                           |
+| 17.4765  | 5400     | 0.0183        | -               | -                           |
+| 17.6386  | 5450     | 0.0052        | -               | -                           |
+| 17.8006  | 5500     | 0.054         | -               | -                           |
+| 17.9627  | 5550     | 0.0273        | -               | -                           |
+| 18.0     | 5562     | -             | 0.1217          | 1.0                         |
+| 18.1232  | 5600     | 0.0102        | -               | -                           |
+| 18.2853  | 5650     | 0.0086        | -               | -                           |
+| 18.4473  | 5700     | 0.0012        | -               | -                           |
+| 18.6094  | 5750     | 0.0084        | -               | -                           |
+| 18.7715  | 5800     | 0.0178        | -               | -                           |
+| 18.9335  | 5850     | 0.0089        | -               | -                           |
+| 19.0     | 5871     | -             | 0.1205          | 1.0000                      |
+| 19.0940  | 5900     | 0.0133        | -               | -                           |
+| 19.2561  | 5950     | 0.0173        | -               | -                           |
+| 19.4182  | 6000     | 0.0129        | -               | -                           |
+| 19.5802  | 6050     | 0.009         | -               | -                           |
+| 19.7423  | 6100     | 0.0019        | -               | -                           |
+| 19.9044  | 6150     | 0.0186        | -               | -                           |
+| 19.9368  | 6160     | -             | 0.1177          | 1.0000                      |
+
+* The bold row denotes the saved checkpoint.
+</details>
+
+### Framework Versions
+- Python: 3.11.9
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.3.0
+- Accelerate: 1.1.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-base",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "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": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.3.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/binary_classification_evaluation_BinaryClassifEval_results.csv

@@ -0,0 +1,85 @@
+epoch,steps,cosine_accuracy,cosine_accuracy_threshold,cosine_f1,cosine_precision,cosine_recall,cosine_f1_threshold,cosine_ap,cosine_mcc
+1.0,78,0.9983766233766234,0.2822584807872772,0.9991876523151909,1.0,0.9983766233766234,0.2822584807872772,1.0,0.0
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+3.0,234,0.9983766233766234,0.13613104820251465,0.9991876523151909,1.0,0.9983766233766234,0.13613104820251465,1.0,0.0
+4.0,312,0.9983766233766234,0.11390984058380127,0.9991876523151909,1.0,0.9983766233766234,0.11390984058380127,1.0,0.0
+5.0,390,0.9983766233766234,0.1044171154499054,0.9991876523151909,1.0,0.9983766233766234,0.1044171154499054,0.9999999999999999,0.0
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+7.0,1085,0.9983766233766234,0.999997079372406,0.9991876523151909,1.0,0.9983766233766234,0.999997079372406,1.0,0.0
+8.0,1240,0.9983766233766234,0.999997615814209,0.9991876523151909,1.0,0.9983766233766234,0.999997615814209,0.9999999999999999,0.0
+9.0,1395,0.9983766233766234,0.9999971389770508,0.9991876523151909,1.0,0.9983766233766234,0.9999971389770508,1.0,0.0
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