Lettria/debug_finetuning_model_tuesday-morning-2
Browse files- README.md +59 -730
- eval/binary_classification_evaluation_BinaryClassifEval_results.csv +2 -11
- eval/similarity_evaluation_EmbeddingSimEval_results.csv +2 -11
- model.safetensors +1 -1
- runs/Feb25_10-51-33_algo-1/events.out.tfevents.1740480693.algo-1.62.0 +3 -0
- runs/Feb25_10-51-33_algo-1/events.out.tfevents.1740480710.algo-1.62.1 +3 -0
- training_args.bin +1 -1
README.md
CHANGED
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@@ -24,86 +24,41 @@ tags:
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- dataset_size:3696
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- loss:MultipleNegativesRankingLoss
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widget:
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- source_sentence:
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sentences:
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et privés (sous-contrat) en Île-de-France'
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- source_sentence: Qui peut bénéficier d'une aide financière de la région Île-de-France
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pour améliorer les infrastructures de transport à proximité des lycées et des
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îles de loisirs?
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sentences:
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- '
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structures professionnelles franciliennes'
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- Pour les instituts déjà autorisés, la demande d’extension de places est à formaliser
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au plus tard le 15 mars 2024
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- 'Le dispositif de développement de la pratique sportive en faveur de tous les
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publics en Île-de-France vise à : Accompagner le développement de la pratique
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sportive pour tous,Favoriser l’accès à la pratique sportive aux femmes, aux personnes
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en situation de handicap, aux adolescents et aux seniors,Soutenir les sportifs
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franciliens dans la recherche de l’excellence,Renforcer la qualité des encadrants
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et de l’intervention des bénévoles,S’attacher au respect de la laïcité et des
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valeurs républicaines,Prévenir les risques de radicalisation,S’assurer de la représentativité
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des femmes dans les instances dirigeantes et dans l’encadrement,Renforcer le lien
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avec les propriétés régionales que sont les îles de loisirs et le Creps,Réduire
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la fracture territoriale avec une attention particulière pour les zones rurales
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et les quartiers politique de la ville'
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- source_sentence: Subventions régionales récentes pour améliorer la qualité de l'air
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dans les établissements publics
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sentences:
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- 'Date de début: Mardi 11 Avril 2023, à 00:00:00 (UTC+0200'
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- 'Sont exclus : Les acquisitions foncières et frais afférents, Les études préalables,
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L''assurance dommage ouvrage, Les travaux de démolition et de dépollution préalables,
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Les travaux de voirie et réseaux divers'
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- 'Type de project: Projets de territoires,Rédaction de chartes,Animation territoriale,Investissements
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liés à l’environnement, la communication, le foncier, la remise en culture,Plateformes
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alimentaires'
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- source_sentence: Quelles sont les entités qui peuvent bénéficier des aides régionales
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pour le développement de projets de méthanisation?
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sentences:
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- 'Bénéficiaires: Professionnel - Agriculture et alimentation, Professionnel - TPE
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< 10, Professionnel - PME < 250, Professionnel - ETI < 5000, Collectivité ou institution
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- Autre (GIP, copropriété, EPA...), Collectivité ou institution - Bailleurs sociaux,
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Collectivité ou institution - Communes de 10 000 à 20 000 hab, Collectivité ou
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institution - Communes de 2000 à 10 000 hab, Collectivité ou institution - Communes
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de < 2000 hab, Collectivité ou institution - Communes de > 20 000 hab, Collectivité
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ou institution - Département, Collectivité ou institution - EPCI, Collectivité
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ou institution - EPT / Métropole du Grand Paris, Collectivité ou institution -
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Office de tourisme intercommunal'
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- 'Pour évaluer votre situation numérique, vous pouvez réaliser en 5 min votre autodiagnostic
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en ligne : Dédié aux commerçants (CCI),Dédié aux artisans (CMA)'
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- 'Nature de l''aide: L’aide prend la forme d’une subvention en investissement.
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La région peut intervenir jusqu’à 70% de votre budget d’investissement, dans la
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limite de 50 000€ de dépenses éligibles'
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model-index:
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- name: BGE base Financial Matryoshka
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results:
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@@ -128,22 +83,22 @@ model-index:
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type: BinaryClassifEval
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metrics:
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- type: cosine_accuracy
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value: 0.
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name: Cosine Accuracy
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- type: cosine_accuracy_threshold
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value:
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name: Cosine Accuracy Threshold
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- type: cosine_f1
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value: 0.
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name: Cosine F1
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- type: cosine_f1_threshold
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value:
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name: Cosine F1 Threshold
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- type: cosine_precision
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value: 1.0
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name: Cosine Precision
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- type: cosine_recall
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value: 0.
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name: Cosine Recall
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- type: cosine_ap
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value: 1.0
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model = SentenceTransformer("model")
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# Run inference
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sentences = [
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'
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'
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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| Metric | Value |
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|:--------------------------|:--------|
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| cosine_accuracy | 0.
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| cosine_accuracy_threshold |
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| cosine_f1 | 0.
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| cosine_f1_threshold |
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| cosine_precision | 1.0 |
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| cosine_recall | 0.
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| **cosine_ap** | **1.0** |
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| cosine_mcc | 0.0 |
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* Size: 3,696 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1
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|:--------|:----------------------------------------------------------------------------------
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| type | string
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| details | <ul><li>min:
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* Samples:
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| sentence1 | sentence2 | label |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
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* Size: 687 evaluation samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 687 samples:
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| | sentence1 | sentence2
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------
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| type | string | string
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| details | <ul><li>min:
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* Samples:
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| sentence1 | sentence2 | label |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
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- `eval_strategy`: epoch
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- `per_device_train_batch_size`: 2
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- `per_device_eval_batch_size`: 2
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- `num_train_epochs`:
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- `lr_scheduler_type`: cosine
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- `warmup_ratio`: 0.1
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- `bf16`: True
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`:
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- `max_steps`: -1
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- `lr_scheduler_type`: cosine
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- `lr_scheduler_kwargs`: {}
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</details>
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### Training Logs
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|:--------:|:---------:|:-------------:|:---------------:|:--------------------------------:|:---------------------------:|
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| 0.0162 | 30 | 0.5038 | - | - | - |
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| 0.0325 | 60 | 0.3814 | - | - | - |
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| 0.0487 | 90 | 0.402 | - | - | - |
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| 0.0649 | 120 | 0.3383 | - | - | - |
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| 0.0812 | 150 | 0.3522 | - | - | - |
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| 0.0974 | 180 | 0.2221 | - | - | - |
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| 0.1136 | 210 | 0.229 | - | - | - |
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| 0.1299 | 240 | 0.3599 | - | - | - |
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| 0.1461 | 270 | 0.1996 | - | - | - |
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| 0.1623 | 300 | 0.1783 | - | - | - |
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| 0.1786 | 330 | 0.2351 | - | - | - |
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| 0.1948 | 360 | 0.3665 | - | - | - |
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| 0.2110 | 390 | 0.3452 | - | - | - |
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| 0.2273 | 420 | 0.2816 | - | - | - |
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| 0.2435 | 450 | 0.1036 | - | - | - |
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| 0.2597 | 480 | 0.1652 | - | - | - |
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| 0.2760 | 510 | 0.2506 | - | - | - |
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| 0.2922 | 540 | 0.1143 | - | - | - |
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| 0.3084 | 570 | 0.3336 | - | - | - |
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| 0.3247 | 600 | 0.2191 | - | - | - |
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| 0.3409 | 630 | 0.1389 | - | - | - |
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| 0.3571 | 660 | 0.2102 | - | - | - |
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| 0.3734 | 690 | 0.2241 | - | - | - |
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| 0.3896 | 720 | 0.3876 | - | - | - |
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| 506 |
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| 0.4058 | 750 | 0.1398 | - | - | - |
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| 0.4221 | 780 | 0.2608 | - | - | - |
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| 0.4383 | 810 | 0.1452 | - | - | - |
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| 509 |
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| 0.4545 | 840 | 0.1657 | - | - | - |
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| 510 |
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| 0.4708 | 870 | 0.2874 | - | - | - |
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| 511 |
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| 0.4870 | 900 | 0.109 | - | - | - |
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| 512 |
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| 0.5032 | 930 | 0.0496 | - | - | - |
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| 513 |
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| 0.5195 | 960 | 0.1891 | - | - | - |
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| 514 |
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| 0.5357 | 990 | 0.1593 | - | - | - |
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| 515 |
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| 0.5519 | 1020 | 0.2214 | - | - | - |
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| 516 |
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| 0.5682 | 1050 | 0.2378 | - | - | - |
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| 517 |
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| 0.5844 | 1080 | 0.0371 | - | - | - |
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| 0.6006 | 1110 | 0.259 | - | - | - |
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| 519 |
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| 0.6169 | 1140 | 0.0274 | - | - | - |
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| 520 |
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| 0.6331 | 1170 | 0.1845 | - | - | - |
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| 521 |
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| 0.6494 | 1200 | 0.1336 | - | - | - |
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| 522 |
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| 0.6656 | 1230 | 0.2105 | - | - | - |
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| 523 |
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| 0.6818 | 1260 | 0.1523 | - | - | - |
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| 0.6981 | 1290 | 0.1659 | - | - | - |
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| 0.7143 | 1320 | 0.0471 | - | - | - |
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| 0.7305 | 1350 | 0.1287 | - | - | - |
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| 0.7468 | 1380 | 0.0914 | - | - | - |
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| 0.7630 | 1410 | 0.2758 | - | - | - |
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| 0.7792 | 1440 | 0.2832 | - | - | - |
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| 0.7955 | 1470 | 0.1038 | - | - | - |
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| 0.8117 | 1500 | 0.1366 | - | - | - |
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| 0.8279 | 1530 | 0.099 | - | - | - |
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| 0.8442 | 1560 | 0.0792 | - | - | - |
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| 0.8604 | 1590 | 0.1524 | - | - | - |
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| 535 |
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| 0.8766 | 1620 | 0.1274 | - | - | - |
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| 536 |
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| 0.8929 | 1650 | 0.0823 | - | - | - |
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| 537 |
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| 0.9091 | 1680 | 0.1655 | - | - | - |
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| 538 |
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| 0.9253 | 1710 | 0.1787 | - | - | - |
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| 539 |
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| 0.9416 | 1740 | 0.2989 | - | - | - |
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| 540 |
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| 0.9578 | 1770 | 0.0582 | - | - | - |
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| 0.9740 | 1800 | 0.1014 | - | - | - |
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| 542 |
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| 0.9903 | 1830 | 0.1914 | - | - | - |
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| 1.0 | 1848 | - | 0.4260 | nan | 1.0 |
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| 544 |
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| 1.0065 | 1860 | 0.0918 | - | - | - |
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| 545 |
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| 1.0227 | 1890 | 0.141 | - | - | - |
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| 546 |
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| 1.0390 | 1920 | 0.084 | - | - | - |
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| 547 |
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| 1.0552 | 1950 | 0.1602 | - | - | - |
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| 548 |
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| 1.0714 | 1980 | 0.2547 | - | - | - |
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| 549 |
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| 1.0877 | 2010 | 0.155 | - | - | - |
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| 1.1039 | 2040 | 0.0279 | - | - | - |
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| 551 |
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| 1.1201 | 2070 | 0.0571 | - | - | - |
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| 552 |
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| 1.1364 | 2100 | 0.253 | - | - | - |
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| 553 |
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| 1.1526 | 2130 | 0.0418 | - | - | - |
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| 554 |
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| 1.1688 | 2160 | 0.3989 | - | - | - |
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| 555 |
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| 1.1851 | 2190 | 0.3349 | - | - | - |
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| 556 |
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| 1.2013 | 2220 | 0.0723 | - | - | - |
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| 557 |
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| 1.2175 | 2250 | 0.0844 | - | - | - |
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| 558 |
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| 1.2338 | 2280 | 0.2263 | - | - | - |
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| 559 |
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| 1.25 | 2310 | 0.2433 | - | - | - |
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| 560 |
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| 1.2662 | 2340 | 0.136 | - | - | - |
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| 561 |
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| 1.2825 | 2370 | 0.0653 | - | - | - |
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| 562 |
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| 1.2987 | 2400 | 0.2757 | - | - | - |
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| 563 |
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| 1.3149 | 2430 | 0.1321 | - | - | - |
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| 564 |
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| 1.3312 | 2460 | 0.2024 | - | - | - |
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| 565 |
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| 1.3474 | 2490 | 0.3687 | - | - | - |
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| 566 |
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| 1.3636 | 2520 | 0.0729 | - | - | - |
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| 567 |
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| 1.3799 | 2550 | 0.1594 | - | - | - |
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| 568 |
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| 1.3961 | 2580 | 0.1276 | - | - | - |
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| 569 |
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| 1.4123 | 2610 | 0.1744 | - | - | - |
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| 570 |
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| 1.4286 | 2640 | 0.2368 | - | - | - |
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| 571 |
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| 1.4448 | 2670 | 0.0535 | - | - | - |
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| 572 |
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| 1.4610 | 2700 | 0.0147 | - | - | - |
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| 573 |
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| 1.4773 | 2730 | 0.125 | - | - | - |
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| 574 |
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| 1.4935 | 2760 | 0.1622 | - | - | - |
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| 575 |
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| 1.5097 | 2790 | 0.0245 | - | - | - |
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| 576 |
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| 1.5260 | 2820 | 0.0637 | - | - | - |
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| 577 |
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| 1.5422 | 2850 | 0.2123 | - | - | - |
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| 578 |
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| 1.5584 | 2880 | 0.0821 | - | - | - |
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| 579 |
-
| 1.5747 | 2910 | 0.3138 | - | - | - |
|
| 580 |
-
| 1.5909 | 2940 | 0.1091 | - | - | - |
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| 581 |
-
| 1.6071 | 2970 | 0.0611 | - | - | - |
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| 582 |
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| 1.6234 | 3000 | 0.0564 | - | - | - |
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| 583 |
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| 1.6396 | 3030 | 0.0578 | - | - | - |
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| 584 |
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| 1.6558 | 3060 | 0.1539 | - | - | - |
|
| 585 |
-
| 1.6721 | 3090 | 0.2868 | - | - | - |
|
| 586 |
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| 1.6883 | 3120 | 0.0766 | - | - | - |
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| 587 |
-
| 1.7045 | 3150 | 0.5037 | - | - | - |
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| 588 |
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| 1.7208 | 3180 | 0.1103 | - | - | - |
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| 589 |
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| 1.7370 | 3210 | 0.3235 | - | - | - |
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| 590 |
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| 1.7532 | 3240 | 0.0225 | - | - | - |
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| 591 |
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| 1.7695 | 3270 | 0.098 | - | - | - |
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| 592 |
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| 1.7857 | 3300 | 0.1235 | - | - | - |
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| 593 |
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| 1.8019 | 3330 | 0.0932 | - | - | - |
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| 594 |
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| 1.8182 | 3360 | 0.0556 | - | - | - |
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| 595 |
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| 1.8344 | 3390 | 0.0265 | - | - | - |
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| 596 |
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| 1.8506 | 3420 | 0.0873 | - | - | - |
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| 597 |
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| 1.8669 | 3450 | 0.0791 | - | - | - |
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| 598 |
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| 1.8831 | 3480 | 0.0347 | - | - | - |
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| 599 |
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| 1.8994 | 3510 | 0.0641 | - | - | - |
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| 600 |
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| 1.9156 | 3540 | 0.292 | - | - | - |
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| 601 |
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| 1.9318 | 3570 | 0.0623 | - | - | - |
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| 602 |
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| 1.9481 | 3600 | 0.1976 | - | - | - |
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| 1.9643 | 3630 | 0.0519 | - | - | - |
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| 1.9805 | 3660 | 0.1408 | - | - | - |
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| 1.9968 | 3690 | 0.1055 | - | - | - |
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| 2.0 | 3696 | - | 0.3302 | nan | 1.0 |
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| 2.0130 | 3720 | 0.0886 | - | - | - |
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| 2.0292 | 3750 | 0.132 | - | - | - |
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| 2.0455 | 3780 | 0.0434 | - | - | - |
|
| 610 |
-
| 2.0617 | 3810 | 0.0119 | - | - | - |
|
| 611 |
-
| 2.0779 | 3840 | 0.0262 | - | - | - |
|
| 612 |
-
| 2.0942 | 3870 | 0.313 | - | - | - |
|
| 613 |
-
| 2.1104 | 3900 | 0.0508 | - | - | - |
|
| 614 |
-
| 2.1266 | 3930 | 0.1431 | - | - | - |
|
| 615 |
-
| 2.1429 | 3960 | 0.1633 | - | - | - |
|
| 616 |
-
| 2.1591 | 3990 | 0.0477 | - | - | - |
|
| 617 |
-
| 2.1753 | 4020 | 0.0563 | - | - | - |
|
| 618 |
-
| 2.1916 | 4050 | 0.0336 | - | - | - |
|
| 619 |
-
| 2.2078 | 4080 | 0.1956 | - | - | - |
|
| 620 |
-
| 2.2240 | 4110 | 0.0948 | - | - | - |
|
| 621 |
-
| 2.2403 | 4140 | 0.1459 | - | - | - |
|
| 622 |
-
| 2.2565 | 4170 | 0.0875 | - | - | - |
|
| 623 |
-
| 2.2727 | 4200 | 0.0571 | - | - | - |
|
| 624 |
-
| 2.2890 | 4230 | 0.0325 | - | - | - |
|
| 625 |
-
| 2.3052 | 4260 | 0.1129 | - | - | - |
|
| 626 |
-
| 2.3214 | 4290 | 0.0622 | - | - | - |
|
| 627 |
-
| 2.3377 | 4320 | 0.3172 | - | - | - |
|
| 628 |
-
| 2.3539 | 4350 | 0.0895 | - | - | - |
|
| 629 |
-
| 2.3701 | 4380 | 0.0803 | - | - | - |
|
| 630 |
-
| 2.3864 | 4410 | 0.0828 | - | - | - |
|
| 631 |
-
| 2.4026 | 4440 | 0.0956 | - | - | - |
|
| 632 |
-
| 2.4188 | 4470 | 0.0322 | - | - | - |
|
| 633 |
-
| 2.4351 | 4500 | 0.0299 | - | - | - |
|
| 634 |
-
| 2.4513 | 4530 | 0.0492 | - | - | - |
|
| 635 |
-
| 2.4675 | 4560 | 0.0405 | - | - | - |
|
| 636 |
-
| 2.4838 | 4590 | 0.012 | - | - | - |
|
| 637 |
-
| 2.5 | 4620 | 0.2303 | - | - | - |
|
| 638 |
-
| 2.5162 | 4650 | 0.0517 | - | - | - |
|
| 639 |
-
| 2.5325 | 4680 | 0.1509 | - | - | - |
|
| 640 |
-
| 2.5487 | 4710 | 0.0284 | - | - | - |
|
| 641 |
-
| 2.5649 | 4740 | 0.1273 | - | - | - |
|
| 642 |
-
| 2.5812 | 4770 | 0.0073 | - | - | - |
|
| 643 |
-
| 2.5974 | 4800 | 0.0083 | - | - | - |
|
| 644 |
-
| 2.6136 | 4830 | 0.0971 | - | - | - |
|
| 645 |
-
| 2.6299 | 4860 | 0.0091 | - | - | - |
|
| 646 |
-
| 2.6461 | 4890 | 0.0512 | - | - | - |
|
| 647 |
-
| 2.6623 | 4920 | 0.0561 | - | - | - |
|
| 648 |
-
| 2.6786 | 4950 | 0.0012 | - | - | - |
|
| 649 |
-
| 2.6948 | 4980 | 0.0152 | - | - | - |
|
| 650 |
-
| 2.7110 | 5010 | 0.0201 | - | - | - |
|
| 651 |
-
| 2.7273 | 5040 | 0.0819 | - | - | - |
|
| 652 |
-
| 2.7435 | 5070 | 0.0547 | - | - | - |
|
| 653 |
-
| 2.7597 | 5100 | 0.0511 | - | - | - |
|
| 654 |
-
| 2.7760 | 5130 | 0.1691 | - | - | - |
|
| 655 |
-
| 2.7922 | 5160 | 0.2579 | - | - | - |
|
| 656 |
-
| 2.8084 | 5190 | 0.0846 | - | - | - |
|
| 657 |
-
| 2.8247 | 5220 | 0.2551 | - | - | - |
|
| 658 |
-
| 2.8409 | 5250 | 0.2625 | - | - | - |
|
| 659 |
-
| 2.8571 | 5280 | 0.1047 | - | - | - |
|
| 660 |
-
| 2.8734 | 5310 | 0.0032 | - | - | - |
|
| 661 |
-
| 2.8896 | 5340 | 0.0967 | - | - | - |
|
| 662 |
-
| 2.9058 | 5370 | 0.1057 | - | - | - |
|
| 663 |
-
| 2.9221 | 5400 | 0.0312 | - | - | - |
|
| 664 |
-
| 2.9383 | 5430 | 0.0237 | - | - | - |
|
| 665 |
-
| 2.9545 | 5460 | 0.091 | - | - | - |
|
| 666 |
-
| 2.9708 | 5490 | 0.1478 | - | - | - |
|
| 667 |
-
| 2.9870 | 5520 | 0.0304 | - | - | - |
|
| 668 |
-
| 3.0 | 5544 | - | 0.3124 | nan | 1.0 |
|
| 669 |
-
| 3.0032 | 5550 | 0.103 | - | - | - |
|
| 670 |
-
| 3.0195 | 5580 | 0.0283 | - | - | - |
|
| 671 |
-
| 3.0357 | 5610 | 0.1117 | - | - | - |
|
| 672 |
-
| 3.0519 | 5640 | 0.0611 | - | - | - |
|
| 673 |
-
| 3.0682 | 5670 | 0.0086 | - | - | - |
|
| 674 |
-
| 3.0844 | 5700 | 0.0684 | - | - | - |
|
| 675 |
-
| 3.1006 | 5730 | 0.021 | - | - | - |
|
| 676 |
-
| 3.1169 | 5760 | 0.0049 | - | - | - |
|
| 677 |
-
| 3.1331 | 5790 | 0.0192 | - | - | - |
|
| 678 |
-
| 3.1494 | 5820 | 0.0641 | - | - | - |
|
| 679 |
-
| 3.1656 | 5850 | 0.0049 | - | - | - |
|
| 680 |
-
| 3.1818 | 5880 | 0.0505 | - | - | - |
|
| 681 |
-
| 3.1981 | 5910 | 0.1081 | - | - | - |
|
| 682 |
-
| 3.2143 | 5940 | 0.0899 | - | - | - |
|
| 683 |
-
| 3.2305 | 5970 | 0.0353 | - | - | - |
|
| 684 |
-
| 3.2468 | 6000 | 0.023 | - | - | - |
|
| 685 |
-
| 3.2630 | 6030 | 0.063 | - | - | - |
|
| 686 |
-
| 3.2792 | 6060 | 0.0098 | - | - | - |
|
| 687 |
-
| 3.2955 | 6090 | 0.0616 | - | - | - |
|
| 688 |
-
| 3.3117 | 6120 | 0.043 | - | - | - |
|
| 689 |
-
| 3.3279 | 6150 | 0.1065 | - | - | - |
|
| 690 |
-
| 3.3442 | 6180 | 0.0261 | - | - | - |
|
| 691 |
-
| 3.3604 | 6210 | 0.0237 | - | - | - |
|
| 692 |
-
| 3.3766 | 6240 | 0.0102 | - | - | - |
|
| 693 |
-
| 3.3929 | 6270 | 0.1165 | - | - | - |
|
| 694 |
-
| 3.4091 | 6300 | 0.0264 | - | - | - |
|
| 695 |
-
| 3.4253 | 6330 | 0.0046 | - | - | - |
|
| 696 |
-
| 3.4416 | 6360 | 0.0396 | - | - | - |
|
| 697 |
-
| 3.4578 | 6390 | 0.0396 | - | - | - |
|
| 698 |
-
| 3.4740 | 6420 | 0.0272 | - | - | - |
|
| 699 |
-
| 3.4903 | 6450 | 0.0351 | - | - | - |
|
| 700 |
-
| 3.5065 | 6480 | 0.052 | - | - | - |
|
| 701 |
-
| 3.5227 | 6510 | 0.0758 | - | - | - |
|
| 702 |
-
| 3.5390 | 6540 | 0.0267 | - | - | - |
|
| 703 |
-
| 3.5552 | 6570 | 0.1482 | - | - | - |
|
| 704 |
-
| 3.5714 | 6600 | 0.0072 | - | - | - |
|
| 705 |
-
| 3.5877 | 6630 | 0.0067 | - | - | - |
|
| 706 |
-
| 3.6039 | 6660 | 0.0061 | - | - | - |
|
| 707 |
-
| 3.6201 | 6690 | 0.1876 | - | - | - |
|
| 708 |
-
| 3.6364 | 6720 | 0.0073 | - | - | - |
|
| 709 |
-
| 3.6526 | 6750 | 0.0734 | - | - | - |
|
| 710 |
-
| 3.6688 | 6780 | 0.0485 | - | - | - |
|
| 711 |
-
| 3.6851 | 6810 | 0.0392 | - | - | - |
|
| 712 |
-
| 3.7013 | 6840 | 0.1201 | - | - | - |
|
| 713 |
-
| 3.7175 | 6870 | 0.023 | - | - | - |
|
| 714 |
-
| 3.7338 | 6900 | 0.0356 | - | - | - |
|
| 715 |
-
| 3.75 | 6930 | 0.0051 | - | - | - |
|
| 716 |
-
| 3.7662 | 6960 | 0.032 | - | - | - |
|
| 717 |
-
| 3.7825 | 6990 | 0.0052 | - | - | - |
|
| 718 |
-
| 3.7987 | 7020 | 0.0128 | - | - | - |
|
| 719 |
-
| 3.8149 | 7050 | 0.0331 | - | - | - |
|
| 720 |
-
| 3.8312 | 7080 | 0.0121 | - | - | - |
|
| 721 |
-
| 3.8474 | 7110 | 0.0535 | - | - | - |
|
| 722 |
-
| 3.8636 | 7140 | 0.0113 | - | - | - |
|
| 723 |
-
| 3.8799 | 7170 | 0.0124 | - | - | - |
|
| 724 |
-
| 3.8961 | 7200 | 0.012 | - | - | - |
|
| 725 |
-
| 3.9123 | 7230 | 0.0029 | - | - | - |
|
| 726 |
-
| 3.9286 | 7260 | 0.1589 | - | - | - |
|
| 727 |
-
| 3.9448 | 7290 | 0.0582 | - | - | - |
|
| 728 |
-
| 3.9610 | 7320 | 0.0423 | - | - | - |
|
| 729 |
-
| 3.9773 | 7350 | 0.015 | - | - | - |
|
| 730 |
-
| 3.9935 | 7380 | 0.1042 | - | - | - |
|
| 731 |
-
| 4.0 | 7392 | - | 0.3149 | nan | 1.0 |
|
| 732 |
-
| 4.0097 | 7410 | 0.01 | - | - | - |
|
| 733 |
-
| 4.0260 | 7440 | 0.0069 | - | - | - |
|
| 734 |
-
| 4.0422 | 7470 | 0.0107 | - | - | - |
|
| 735 |
-
| 4.0584 | 7500 | 0.0167 | - | - | - |
|
| 736 |
-
| 4.0747 | 7530 | 0.1177 | - | - | - |
|
| 737 |
-
| 4.0909 | 7560 | 0.0679 | - | - | - |
|
| 738 |
-
| 4.1071 | 7590 | 0.0484 | - | - | - |
|
| 739 |
-
| 4.1234 | 7620 | 0.0251 | - | - | - |
|
| 740 |
-
| 4.1396 | 7650 | 0.0272 | - | - | - |
|
| 741 |
-
| 4.1558 | 7680 | 0.0688 | - | - | - |
|
| 742 |
-
| 4.1721 | 7710 | 0.0192 | - | - | - |
|
| 743 |
-
| 4.1883 | 7740 | 0.0321 | - | - | - |
|
| 744 |
-
| 4.2045 | 7770 | 0.0043 | - | - | - |
|
| 745 |
-
| 4.2208 | 7800 | 0.0882 | - | - | - |
|
| 746 |
-
| 4.2370 | 7830 | 0.0074 | - | - | - |
|
| 747 |
-
| 4.2532 | 7860 | 0.0214 | - | - | - |
|
| 748 |
-
| 4.2695 | 7890 | 0.0003 | - | - | - |
|
| 749 |
-
| 4.2857 | 7920 | 0.0128 | - | - | - |
|
| 750 |
-
| 4.3019 | 7950 | 0.0063 | - | - | - |
|
| 751 |
-
| 4.3182 | 7980 | 0.0342 | - | - | - |
|
| 752 |
-
| 4.3344 | 8010 | 0.0099 | - | - | - |
|
| 753 |
-
| 4.3506 | 8040 | 0.0574 | - | - | - |
|
| 754 |
-
| 4.3669 | 8070 | 0.1595 | - | - | - |
|
| 755 |
-
| 4.3831 | 8100 | 0.0028 | - | - | - |
|
| 756 |
-
| 4.3994 | 8130 | 0.0051 | - | - | - |
|
| 757 |
-
| 4.4156 | 8160 | 0.0029 | - | - | - |
|
| 758 |
-
| 4.4318 | 8190 | 0.0085 | - | - | - |
|
| 759 |
-
| 4.4481 | 8220 | 0.0154 | - | - | - |
|
| 760 |
-
| 4.4643 | 8250 | 0.0063 | - | - | - |
|
| 761 |
-
| 4.4805 | 8280 | 0.0222 | - | - | - |
|
| 762 |
-
| 4.4968 | 8310 | 0.0083 | - | - | - |
|
| 763 |
-
| 4.5130 | 8340 | 0.0527 | - | - | - |
|
| 764 |
-
| 4.5292 | 8370 | 0.0489 | - | - | - |
|
| 765 |
-
| 4.5455 | 8400 | 0.0888 | - | - | - |
|
| 766 |
-
| 4.5617 | 8430 | 0.0312 | - | - | - |
|
| 767 |
-
| 4.5779 | 8460 | 0.0961 | - | - | - |
|
| 768 |
-
| 4.5942 | 8490 | 0.0528 | - | - | - |
|
| 769 |
-
| 4.6104 | 8520 | 0.0039 | - | - | - |
|
| 770 |
-
| 4.6266 | 8550 | 0.0073 | - | - | - |
|
| 771 |
-
| 4.6429 | 8580 | 0.0613 | - | - | - |
|
| 772 |
-
| 4.6591 | 8610 | 0.0214 | - | - | - |
|
| 773 |
-
| 4.6753 | 8640 | 0.0113 | - | - | - |
|
| 774 |
-
| 4.6916 | 8670 | 0.0105 | - | - | - |
|
| 775 |
-
| 4.7078 | 8700 | 0.0014 | - | - | - |
|
| 776 |
-
| 4.7240 | 8730 | 0.0054 | - | - | - |
|
| 777 |
-
| 4.7403 | 8760 | 0.0094 | - | - | - |
|
| 778 |
-
| 4.7565 | 8790 | 0.0263 | - | - | - |
|
| 779 |
-
| 4.7727 | 8820 | 0.0497 | - | - | - |
|
| 780 |
-
| 4.7890 | 8850 | 0.0441 | - | - | - |
|
| 781 |
-
| 4.8052 | 8880 | 0.1178 | - | - | - |
|
| 782 |
-
| 4.8214 | 8910 | 0.0019 | - | - | - |
|
| 783 |
-
| 4.8377 | 8940 | 0.073 | - | - | - |
|
| 784 |
-
| 4.8539 | 8970 | 0.0273 | - | - | - |
|
| 785 |
-
| 4.8701 | 9000 | 0.0167 | - | - | - |
|
| 786 |
-
| 4.8864 | 9030 | 0.0017 | - | - | - |
|
| 787 |
-
| 4.9026 | 9060 | 0.0198 | - | - | - |
|
| 788 |
-
| 4.9188 | 9090 | 0.0249 | - | - | - |
|
| 789 |
-
| 4.9351 | 9120 | 0.0326 | - | - | - |
|
| 790 |
-
| 4.9513 | 9150 | 0.0326 | - | - | - |
|
| 791 |
-
| 4.9675 | 9180 | 0.0013 | - | - | - |
|
| 792 |
-
| 4.9838 | 9210 | 0.0039 | - | - | - |
|
| 793 |
-
| 5.0 | 9240 | 0.0501 | 0.2695 | nan | 1.0 |
|
| 794 |
-
| 5.0162 | 9270 | 0.011 | - | - | - |
|
| 795 |
-
| 5.0325 | 9300 | 0.0203 | - | - | - |
|
| 796 |
-
| 5.0487 | 9330 | 0.0349 | - | - | - |
|
| 797 |
-
| 5.0649 | 9360 | 0.0287 | - | - | - |
|
| 798 |
-
| 5.0812 | 9390 | 0.0085 | - | - | - |
|
| 799 |
-
| 5.0974 | 9420 | 0.0329 | - | - | - |
|
| 800 |
-
| 5.1136 | 9450 | 0.0006 | - | - | - |
|
| 801 |
-
| 5.1299 | 9480 | 0.1229 | - | - | - |
|
| 802 |
-
| 5.1461 | 9510 | 0.0097 | - | - | - |
|
| 803 |
-
| 5.1623 | 9540 | 0.0596 | - | - | - |
|
| 804 |
-
| 5.1786 | 9570 | 0.0273 | - | - | - |
|
| 805 |
-
| 5.1948 | 9600 | 0.0043 | - | - | - |
|
| 806 |
-
| 5.2110 | 9630 | 0.0173 | - | - | - |
|
| 807 |
-
| 5.2273 | 9660 | 0.0052 | - | - | - |
|
| 808 |
-
| 5.2435 | 9690 | 0.0013 | - | - | - |
|
| 809 |
-
| 5.2597 | 9720 | 0.0501 | - | - | - |
|
| 810 |
-
| 5.2760 | 9750 | 0.0188 | - | - | - |
|
| 811 |
-
| 5.2922 | 9780 | 0.002 | - | - | - |
|
| 812 |
-
| 5.3084 | 9810 | 0.0031 | - | - | - |
|
| 813 |
-
| 5.3247 | 9840 | 0.0571 | - | - | - |
|
| 814 |
-
| 5.3409 | 9870 | 0.0128 | - | - | - |
|
| 815 |
-
| 5.3571 | 9900 | 0.0338 | - | - | - |
|
| 816 |
-
| 5.3734 | 9930 | 0.0028 | - | - | - |
|
| 817 |
-
| 5.3896 | 9960 | 0.002 | - | - | - |
|
| 818 |
-
| 5.4058 | 9990 | 0.0261 | - | - | - |
|
| 819 |
-
| 5.4221 | 10020 | 0.0099 | - | - | - |
|
| 820 |
-
| 5.4383 | 10050 | 0.0031 | - | - | - |
|
| 821 |
-
| 5.4545 | 10080 | 0.0331 | - | - | - |
|
| 822 |
-
| 5.4708 | 10110 | 0.0538 | - | - | - |
|
| 823 |
-
| 5.4870 | 10140 | 0.0006 | - | - | - |
|
| 824 |
-
| 5.5032 | 10170 | 0.0364 | - | - | - |
|
| 825 |
-
| 5.5195 | 10200 | 0.0508 | - | - | - |
|
| 826 |
-
| 5.5357 | 10230 | 0.0966 | - | - | - |
|
| 827 |
-
| 5.5519 | 10260 | 0.0036 | - | - | - |
|
| 828 |
-
| 5.5682 | 10290 | 0.0035 | - | - | - |
|
| 829 |
-
| 5.5844 | 10320 | 0.0016 | - | - | - |
|
| 830 |
-
| 5.6006 | 10350 | 0.0269 | - | - | - |
|
| 831 |
-
| 5.6169 | 10380 | 0.0006 | - | - | - |
|
| 832 |
-
| 5.6331 | 10410 | 0.0179 | - | - | - |
|
| 833 |
-
| 5.6494 | 10440 | 0.0095 | - | - | - |
|
| 834 |
-
| 5.6656 | 10470 | 0.0958 | - | - | - |
|
| 835 |
-
| 5.6818 | 10500 | 0.0121 | - | - | - |
|
| 836 |
-
| 5.6981 | 10530 | 0.0009 | - | - | - |
|
| 837 |
-
| 5.7143 | 10560 | 0.055 | - | - | - |
|
| 838 |
-
| 5.7305 | 10590 | 0.012 | - | - | - |
|
| 839 |
-
| 5.7468 | 10620 | 0.0182 | - | - | - |
|
| 840 |
-
| 5.7630 | 10650 | 0.0002 | - | - | - |
|
| 841 |
-
| 5.7792 | 10680 | 0.0563 | - | - | - |
|
| 842 |
-
| 5.7955 | 10710 | 0.0317 | - | - | - |
|
| 843 |
-
| 5.8117 | 10740 | 0.0083 | - | - | - |
|
| 844 |
-
| 5.8279 | 10770 | 0.0093 | - | - | - |
|
| 845 |
-
| 5.8442 | 10800 | 0.0626 | - | - | - |
|
| 846 |
-
| 5.8604 | 10830 | 0.0266 | - | - | - |
|
| 847 |
-
| 5.8766 | 10860 | 0.0033 | - | - | - |
|
| 848 |
-
| 5.8929 | 10890 | 0.0003 | - | - | - |
|
| 849 |
-
| 5.9091 | 10920 | 0.0025 | - | - | - |
|
| 850 |
-
| 5.9253 | 10950 | 0.008 | - | - | - |
|
| 851 |
-
| 5.9416 | 10980 | 0.0132 | - | - | - |
|
| 852 |
-
| 5.9578 | 11010 | 0.0047 | - | - | - |
|
| 853 |
-
| 5.9740 | 11040 | 0.0292 | - | - | - |
|
| 854 |
-
| 5.9903 | 11070 | 0.0042 | - | - | - |
|
| 855 |
-
| 6.0 | 11088 | - | 0.3194 | nan | 1.0 |
|
| 856 |
-
| 6.0065 | 11100 | 0.0066 | - | - | - |
|
| 857 |
-
| 6.0227 | 11130 | 0.0054 | - | - | - |
|
| 858 |
-
| 6.0390 | 11160 | 0.0007 | - | - | - |
|
| 859 |
-
| 6.0552 | 11190 | 0.0114 | - | - | - |
|
| 860 |
-
| 6.0714 | 11220 | 0.0007 | - | - | - |
|
| 861 |
-
| 6.0877 | 11250 | 0.0214 | - | - | - |
|
| 862 |
-
| 6.1039 | 11280 | 0.0043 | - | - | - |
|
| 863 |
-
| 6.1201 | 11310 | 0.0128 | - | - | - |
|
| 864 |
-
| 6.1364 | 11340 | 0.0119 | - | - | - |
|
| 865 |
-
| 6.1526 | 11370 | 0.0125 | - | - | - |
|
| 866 |
-
| 6.1688 | 11400 | 0.0066 | - | - | - |
|
| 867 |
-
| 6.1851 | 11430 | 0.0005 | - | - | - |
|
| 868 |
-
| 6.2013 | 11460 | 0.0617 | - | - | - |
|
| 869 |
-
| 6.2175 | 11490 | 0.0031 | - | - | - |
|
| 870 |
-
| 6.2338 | 11520 | 0.0008 | - | - | - |
|
| 871 |
-
| 6.25 | 11550 | 0.0261 | - | - | - |
|
| 872 |
-
| 6.2662 | 11580 | 0.0204 | - | - | - |
|
| 873 |
-
| 6.2825 | 11610 | 0.0015 | - | - | - |
|
| 874 |
-
| 6.2987 | 11640 | 0.0002 | - | - | - |
|
| 875 |
-
| 6.3149 | 11670 | 0.0035 | - | - | - |
|
| 876 |
-
| 6.3312 | 11700 | 0.0046 | - | - | - |
|
| 877 |
-
| 6.3474 | 11730 | 0.0521 | - | - | - |
|
| 878 |
-
| 6.3636 | 11760 | 0.0001 | - | - | - |
|
| 879 |
-
| 6.3799 | 11790 | 0.0023 | - | - | - |
|
| 880 |
-
| 6.3961 | 11820 | 0.0063 | - | - | - |
|
| 881 |
-
| 6.4123 | 11850 | 0.002 | - | - | - |
|
| 882 |
-
| 6.4286 | 11880 | 0.0015 | - | - | - |
|
| 883 |
-
| 6.4448 | 11910 | 0.0166 | - | - | - |
|
| 884 |
-
| 6.4610 | 11940 | 0.0077 | - | - | - |
|
| 885 |
-
| 6.4773 | 11970 | 0.0076 | - | - | - |
|
| 886 |
-
| 6.4935 | 12000 | 0.004 | - | - | - |
|
| 887 |
-
| 6.5097 | 12030 | 0.0785 | - | - | - |
|
| 888 |
-
| 6.5260 | 12060 | 0.0106 | - | - | - |
|
| 889 |
-
| 6.5422 | 12090 | 0.0006 | - | - | - |
|
| 890 |
-
| 6.5584 | 12120 | 0.0273 | - | - | - |
|
| 891 |
-
| 6.5747 | 12150 | 0.0236 | - | - | - |
|
| 892 |
-
| 6.5909 | 12180 | 0.006 | - | - | - |
|
| 893 |
-
| 6.6071 | 12210 | 0.0483 | - | - | - |
|
| 894 |
-
| 6.6234 | 12240 | 0.0095 | - | - | - |
|
| 895 |
-
| 6.6396 | 12270 | 0.0007 | - | - | - |
|
| 896 |
-
| 6.6558 | 12300 | 0.0216 | - | - | - |
|
| 897 |
-
| 6.6721 | 12330 | 0.0623 | - | - | - |
|
| 898 |
-
| 6.6883 | 12360 | 0.0064 | - | - | - |
|
| 899 |
-
| 6.7045 | 12390 | 0.0175 | - | - | - |
|
| 900 |
-
| 6.7208 | 12420 | 0.0093 | - | - | - |
|
| 901 |
-
| 6.7370 | 12450 | 0.0118 | - | - | - |
|
| 902 |
-
| 6.7532 | 12480 | 0.0155 | - | - | - |
|
| 903 |
-
| 6.7695 | 12510 | 0.0004 | - | - | - |
|
| 904 |
-
| 6.7857 | 12540 | 0.0356 | - | - | - |
|
| 905 |
-
| 6.8019 | 12570 | 0.0018 | - | - | - |
|
| 906 |
-
| 6.8182 | 12600 | 0.0092 | - | - | - |
|
| 907 |
-
| 6.8344 | 12630 | 0.0079 | - | - | - |
|
| 908 |
-
| 6.8506 | 12660 | 0.0058 | - | - | - |
|
| 909 |
-
| 6.8669 | 12690 | 0.0089 | - | - | - |
|
| 910 |
-
| 6.8831 | 12720 | 0.0125 | - | - | - |
|
| 911 |
-
| 6.8994 | 12750 | 0.001 | - | - | - |
|
| 912 |
-
| 6.9156 | 12780 | 0.0177 | - | - | - |
|
| 913 |
-
| 6.9318 | 12810 | 0.0076 | - | - | - |
|
| 914 |
-
| 6.9481 | 12840 | 0.0366 | - | - | - |
|
| 915 |
-
| 6.9643 | 12870 | 0.0698 | - | - | - |
|
| 916 |
-
| 6.9805 | 12900 | 0.0275 | - | - | - |
|
| 917 |
-
| 6.9968 | 12930 | 0.0032 | - | - | - |
|
| 918 |
-
| 7.0 | 12936 | - | 0.2245 | nan | 1.0 |
|
| 919 |
-
| 7.0130 | 12960 | 0.0 | - | - | - |
|
| 920 |
-
| 7.0292 | 12990 | 0.0043 | - | - | - |
|
| 921 |
-
| 7.0455 | 13020 | 0.0073 | - | - | - |
|
| 922 |
-
| 7.0617 | 13050 | 0.0012 | - | - | - |
|
| 923 |
-
| 7.0779 | 13080 | 0.0021 | - | - | - |
|
| 924 |
-
| 7.0942 | 13110 | 0.0018 | - | - | - |
|
| 925 |
-
| 7.1104 | 13140 | 0.0 | - | - | - |
|
| 926 |
-
| 7.1266 | 13170 | 0.001 | - | - | - |
|
| 927 |
-
| 7.1429 | 13200 | 0.0006 | - | - | - |
|
| 928 |
-
| 7.1591 | 13230 | 0.0092 | - | - | - |
|
| 929 |
-
| 7.1753 | 13260 | 0.0002 | - | - | - |
|
| 930 |
-
| 7.1916 | 13290 | 0.0148 | - | - | - |
|
| 931 |
-
| 7.2078 | 13320 | 0.0019 | - | - | - |
|
| 932 |
-
| 7.2240 | 13350 | 0.0333 | - | - | - |
|
| 933 |
-
| 7.2403 | 13380 | 0.0011 | - | - | - |
|
| 934 |
-
| 7.2565 | 13410 | 0.0112 | - | - | - |
|
| 935 |
-
| 7.2727 | 13440 | 0.0014 | - | - | - |
|
| 936 |
-
| 7.2890 | 13470 | 0.0215 | - | - | - |
|
| 937 |
-
| 7.3052 | 13500 | 0.0013 | - | - | - |
|
| 938 |
-
| 7.3214 | 13530 | 0.0051 | - | - | - |
|
| 939 |
-
| 7.3377 | 13560 | 0.0013 | - | - | - |
|
| 940 |
-
| 7.3539 | 13590 | 0.0271 | - | - | - |
|
| 941 |
-
| 7.3701 | 13620 | 0.0004 | - | - | - |
|
| 942 |
-
| 7.3864 | 13650 | 0.0029 | - | - | - |
|
| 943 |
-
| 7.4026 | 13680 | 0.0021 | - | - | - |
|
| 944 |
-
| 7.4188 | 13710 | 0.0007 | - | - | - |
|
| 945 |
-
| 7.4351 | 13740 | 0.0027 | - | - | - |
|
| 946 |
-
| 7.4513 | 13770 | 0.0003 | - | - | - |
|
| 947 |
-
| 7.4675 | 13800 | 0.0574 | - | - | - |
|
| 948 |
-
| 7.4838 | 13830 | 0.0002 | - | - | - |
|
| 949 |
-
| 7.5 | 13860 | 0.0363 | - | - | - |
|
| 950 |
-
| 7.5162 | 13890 | 0.0073 | - | - | - |
|
| 951 |
-
| 7.5325 | 13920 | 0.002 | - | - | - |
|
| 952 |
-
| 7.5487 | 13950 | 0.0233 | - | - | - |
|
| 953 |
-
| 7.5649 | 13980 | 0.0098 | - | - | - |
|
| 954 |
-
| 7.5812 | 14010 | 0.0123 | - | - | - |
|
| 955 |
-
| 7.5974 | 14040 | 0.018 | - | - | - |
|
| 956 |
-
| 7.6136 | 14070 | 0.0101 | - | - | - |
|
| 957 |
-
| 7.6299 | 14100 | 0.0108 | - | - | - |
|
| 958 |
-
| 7.6461 | 14130 | 0.0205 | - | - | - |
|
| 959 |
-
| 7.6623 | 14160 | 0.002 | - | - | - |
|
| 960 |
-
| 7.6786 | 14190 | 0.0002 | - | - | - |
|
| 961 |
-
| 7.6948 | 14220 | 0.0002 | - | - | - |
|
| 962 |
-
| 7.7110 | 14250 | 0.0166 | - | - | - |
|
| 963 |
-
| 7.7273 | 14280 | 0.0024 | - | - | - |
|
| 964 |
-
| 7.7435 | 14310 | 0.0001 | - | - | - |
|
| 965 |
-
| 7.7597 | 14340 | 0.0001 | - | - | - |
|
| 966 |
-
| 7.7760 | 14370 | 0.0013 | - | - | - |
|
| 967 |
-
| 7.7922 | 14400 | 0.0063 | - | - | - |
|
| 968 |
-
| 7.8084 | 14430 | 0.014 | - | - | - |
|
| 969 |
-
| 7.8247 | 14460 | 0.0137 | - | - | - |
|
| 970 |
-
| 7.8409 | 14490 | 0.0002 | - | - | - |
|
| 971 |
-
| 7.8571 | 14520 | 0.0001 | - | - | - |
|
| 972 |
-
| 7.8734 | 14550 | 0.0144 | - | - | - |
|
| 973 |
-
| 7.8896 | 14580 | 0.004 | - | - | - |
|
| 974 |
-
| 7.9058 | 14610 | 0.056 | - | - | - |
|
| 975 |
-
| 7.9221 | 14640 | 0.03 | - | - | - |
|
| 976 |
-
| 7.9383 | 14670 | 0.076 | - | - | - |
|
| 977 |
-
| 7.9545 | 14700 | 0.0009 | - | - | - |
|
| 978 |
-
| 7.9708 | 14730 | 0.0017 | - | - | - |
|
| 979 |
-
| 7.9870 | 14760 | 0.0196 | - | - | - |
|
| 980 |
-
| 8.0 | 14784 | - | 0.2271 | nan | 1.0 |
|
| 981 |
-
| 8.0032 | 14790 | 0.0162 | - | - | - |
|
| 982 |
-
| 8.0195 | 14820 | 0.0001 | - | - | - |
|
| 983 |
-
| 8.0357 | 14850 | 0.0081 | - | - | - |
|
| 984 |
-
| 8.0519 | 14880 | 0.001 | - | - | - |
|
| 985 |
-
| 8.0682 | 14910 | 0.0017 | - | - | - |
|
| 986 |
-
| 8.0844 | 14940 | 0.0 | - | - | - |
|
| 987 |
-
| 8.1006 | 14970 | 0.0001 | - | - | - |
|
| 988 |
-
| 8.1169 | 15000 | 0.0083 | - | - | - |
|
| 989 |
-
| 8.1331 | 15030 | 0.0121 | - | - | - |
|
| 990 |
-
| 8.1494 | 15060 | 0.0469 | - | - | - |
|
| 991 |
-
| 8.1656 | 15090 | 0.0003 | - | - | - |
|
| 992 |
-
| 8.1818 | 15120 | 0.0002 | - | - | - |
|
| 993 |
-
| 8.1981 | 15150 | 0.0001 | - | - | - |
|
| 994 |
-
| 8.2143 | 15180 | 0.0004 | - | - | - |
|
| 995 |
-
| 8.2305 | 15210 | 0.039 | - | - | - |
|
| 996 |
-
| 8.2468 | 15240 | 0.0053 | - | - | - |
|
| 997 |
-
| 8.2630 | 15270 | 0.0065 | - | - | - |
|
| 998 |
-
| 8.2792 | 15300 | 0.0002 | - | - | - |
|
| 999 |
-
| 8.2955 | 15330 | 0.0072 | - | - | - |
|
| 1000 |
-
| 8.3117 | 15360 | 0.0046 | - | - | - |
|
| 1001 |
-
| 8.3279 | 15390 | 0.0001 | - | - | - |
|
| 1002 |
-
| 8.3442 | 15420 | 0.0001 | - | - | - |
|
| 1003 |
-
| 8.3604 | 15450 | 0.0451 | - | - | - |
|
| 1004 |
-
| 8.3766 | 15480 | 0.015 | - | - | - |
|
| 1005 |
-
| 8.3929 | 15510 | 0.0009 | - | - | - |
|
| 1006 |
-
| 8.4091 | 15540 | 0.0185 | - | - | - |
|
| 1007 |
-
| 8.4253 | 15570 | 0.0018 | - | - | - |
|
| 1008 |
-
| 8.4416 | 15600 | 0.018 | - | - | - |
|
| 1009 |
-
| 8.4578 | 15630 | 0.0055 | - | - | - |
|
| 1010 |
-
| 8.4740 | 15660 | 0.0011 | - | - | - |
|
| 1011 |
-
| 8.4903 | 15690 | 0.0 | - | - | - |
|
| 1012 |
-
| 8.5065 | 15720 | 0.0002 | - | - | - |
|
| 1013 |
-
| 8.5227 | 15750 | 0.0151 | - | - | - |
|
| 1014 |
-
| 8.5390 | 15780 | 0.0151 | - | - | - |
|
| 1015 |
-
| 8.5552 | 15810 | 0.0019 | - | - | - |
|
| 1016 |
-
| 8.5714 | 15840 | 0.0 | - | - | - |
|
| 1017 |
-
| 8.5877 | 15870 | 0.0002 | - | - | - |
|
| 1018 |
-
| 8.6039 | 15900 | 0.0 | - | - | - |
|
| 1019 |
-
| 8.6201 | 15930 | 0.0272 | - | - | - |
|
| 1020 |
-
| 8.6364 | 15960 | 0.0004 | - | - | - |
|
| 1021 |
-
| 8.6526 | 15990 | 0.0 | - | - | - |
|
| 1022 |
-
| 8.6688 | 16020 | 0.0057 | - | - | - |
|
| 1023 |
-
| 8.6851 | 16050 | 0.0003 | - | - | - |
|
| 1024 |
-
| 8.7013 | 16080 | 0.0268 | - | - | - |
|
| 1025 |
-
| 8.7175 | 16110 | 0.0007 | - | - | - |
|
| 1026 |
-
| 8.7338 | 16140 | 0.1138 | - | - | - |
|
| 1027 |
-
| 8.75 | 16170 | 0.0001 | - | - | - |
|
| 1028 |
-
| 8.7662 | 16200 | 0.0002 | - | - | - |
|
| 1029 |
-
| 8.7825 | 16230 | 0.0008 | - | - | - |
|
| 1030 |
-
| 8.7987 | 16260 | 0.0003 | - | - | - |
|
| 1031 |
-
| 8.8149 | 16290 | 0.0002 | - | - | - |
|
| 1032 |
-
| 8.8312 | 16320 | 0.0281 | - | - | - |
|
| 1033 |
-
| 8.8474 | 16350 | 0.0056 | - | - | - |
|
| 1034 |
-
| 8.8636 | 16380 | 0.0002 | - | - | - |
|
| 1035 |
-
| 8.8799 | 16410 | 0.0004 | - | - | - |
|
| 1036 |
-
| 8.8961 | 16440 | 0.0003 | - | - | - |
|
| 1037 |
-
| 8.9123 | 16470 | 0.0001 | - | - | - |
|
| 1038 |
-
| 8.9286 | 16500 | 0.0001 | - | - | - |
|
| 1039 |
-
| 8.9448 | 16530 | 0.0 | - | - | - |
|
| 1040 |
-
| 8.9610 | 16560 | 0.0 | - | - | - |
|
| 1041 |
-
| 8.9773 | 16590 | 0.0009 | - | - | - |
|
| 1042 |
-
| 8.9935 | 16620 | 0.0011 | - | - | - |
|
| 1043 |
-
| 9.0 | 16632 | - | 0.2189 | nan | 1.0 |
|
| 1044 |
-
| 9.0097 | 16650 | 0.0 | - | - | - |
|
| 1045 |
-
| 9.0260 | 16680 | 0.0011 | - | - | - |
|
| 1046 |
-
| 9.0422 | 16710 | 0.0017 | - | - | - |
|
| 1047 |
-
| 9.0584 | 16740 | 0.0205 | - | - | - |
|
| 1048 |
-
| 9.0747 | 16770 | 0.0143 | - | - | - |
|
| 1049 |
-
| 9.0909 | 16800 | 0.0005 | - | - | - |
|
| 1050 |
-
| 9.1071 | 16830 | 0.0001 | - | - | - |
|
| 1051 |
-
| 9.1234 | 16860 | 0.0112 | - | - | - |
|
| 1052 |
-
| 9.1396 | 16890 | 0.0 | - | - | - |
|
| 1053 |
-
| 9.1558 | 16920 | 0.0001 | - | - | - |
|
| 1054 |
-
| 9.1721 | 16950 | 0.0003 | - | - | - |
|
| 1055 |
-
| 9.1883 | 16980 | 0.0237 | - | - | - |
|
| 1056 |
-
| 9.2045 | 17010 | 0.0002 | - | - | - |
|
| 1057 |
-
| 9.2208 | 17040 | 0.0018 | - | - | - |
|
| 1058 |
-
| 9.2370 | 17070 | 0.0018 | - | - | - |
|
| 1059 |
-
| 9.2532 | 17100 | 0.0125 | - | - | - |
|
| 1060 |
-
| 9.2695 | 17130 | 0.0001 | - | - | - |
|
| 1061 |
-
| 9.2857 | 17160 | 0.0016 | - | - | - |
|
| 1062 |
-
| 9.3019 | 17190 | 0.0024 | - | - | - |
|
| 1063 |
-
| 9.3182 | 17220 | 0.0268 | - | - | - |
|
| 1064 |
-
| 9.3344 | 17250 | 0.0011 | - | - | - |
|
| 1065 |
-
| 9.3506 | 17280 | 0.0002 | - | - | - |
|
| 1066 |
-
| 9.3669 | 17310 | 0.0018 | - | - | - |
|
| 1067 |
-
| 9.3831 | 17340 | 0.003 | - | - | - |
|
| 1068 |
-
| 9.3994 | 17370 | 0.0144 | - | - | - |
|
| 1069 |
-
| 9.4156 | 17400 | 0.0222 | - | - | - |
|
| 1070 |
-
| 9.4318 | 17430 | 0.0083 | - | - | - |
|
| 1071 |
-
| 9.4481 | 17460 | 0.0011 | - | - | - |
|
| 1072 |
-
| 9.4643 | 17490 | 0.0015 | - | - | - |
|
| 1073 |
-
| 9.4805 | 17520 | 0.004 | - | - | - |
|
| 1074 |
-
| 9.4968 | 17550 | 0.0021 | - | - | - |
|
| 1075 |
-
| 9.5130 | 17580 | 0.0 | - | - | - |
|
| 1076 |
-
| 9.5292 | 17610 | 0.0021 | - | - | - |
|
| 1077 |
-
| 9.5455 | 17640 | 0.0009 | - | - | - |
|
| 1078 |
-
| 9.5617 | 17670 | 0.0161 | - | - | - |
|
| 1079 |
-
| 9.5779 | 17700 | 0.001 | - | - | - |
|
| 1080 |
-
| 9.5942 | 17730 | 0.0257 | - | - | - |
|
| 1081 |
-
| 9.6104 | 17760 | 0.0002 | - | - | - |
|
| 1082 |
-
| 9.6266 | 17790 | 0.0009 | - | - | - |
|
| 1083 |
-
| 9.6429 | 17820 | 0.0442 | - | - | - |
|
| 1084 |
-
| 9.6591 | 17850 | 0.0011 | - | - | - |
|
| 1085 |
-
| 9.6753 | 17880 | 0.0016 | - | - | - |
|
| 1086 |
-
| 9.6916 | 17910 | 0.0196 | - | - | - |
|
| 1087 |
-
| 9.7078 | 17940 | 0.0144 | - | - | - |
|
| 1088 |
-
| 9.7240 | 17970 | 0.0 | - | - | - |
|
| 1089 |
-
| 9.7403 | 18000 | 0.0001 | - | - | - |
|
| 1090 |
-
| 9.7565 | 18030 | 0.004 | - | - | - |
|
| 1091 |
-
| 9.7727 | 18060 | 0.0001 | - | - | - |
|
| 1092 |
-
| 9.7890 | 18090 | 0.0013 | - | - | - |
|
| 1093 |
-
| 9.8052 | 18120 | 0.0024 | - | - | - |
|
| 1094 |
-
| 9.8214 | 18150 | 0.0044 | - | - | - |
|
| 1095 |
-
| 9.8377 | 18180 | 0.0005 | - | - | - |
|
| 1096 |
-
| 9.8539 | 18210 | 0.0 | - | - | - |
|
| 1097 |
-
| 9.8701 | 18240 | 0.0176 | - | - | - |
|
| 1098 |
-
| 9.8864 | 18270 | 0.0007 | - | - | - |
|
| 1099 |
-
| 9.9026 | 18300 | 0.0001 | - | - | - |
|
| 1100 |
-
| 9.9188 | 18330 | 0.0003 | - | - | - |
|
| 1101 |
-
| 9.9351 | 18360 | 0.0091 | - | - | - |
|
| 1102 |
-
| 9.9513 | 18390 | 0.0025 | - | - | - |
|
| 1103 |
-
| 9.9675 | 18420 | 0.0006 | - | - | - |
|
| 1104 |
-
| 9.9838 | 18450 | 0.0 | - | - | - |
|
| 1105 |
-
| **10.0** | **18480** | **0.0107** | **0.2172** | **nan** | **1.0** |
|
| 1106 |
|
| 1107 |
* The bold row denotes the saved checkpoint.
|
| 1108 |
-
</details>
|
| 1109 |
|
| 1110 |
### Framework Versions
|
| 1111 |
- Python: 3.11.9
|
|
|
|
| 24 |
- dataset_size:3696
|
| 25 |
- loss:MultipleNegativesRankingLoss
|
| 26 |
widget:
|
| 27 |
+
- source_sentence: Quel est le montant du cofinancement que la Région IDF propose
|
| 28 |
+
pour une allocation doctorale ?
|
| 29 |
sentences:
|
| 30 |
+
- sur des projets comportant une dimension numérique sur les thématiques ci-dessous
|
| 31 |
+
détaillées dans le texte de l'appel à projets :A - Économie circulaire,B - Cancer
|
| 32 |
+
pédiatrique,C - Autisme,D - Santé environnementale,E - Vieillissement
|
| 33 |
+
- 'bénéficiaires: Le dispositif est ouvert aux réseaux structurants qui fédèrent
|
| 34 |
+
des professionnels et des acteurs du secteur du patrimoine : associations et fondations.
|
| 35 |
+
Les effectifs d’adhérents doivent être représentatifs à l’échelle du territoire
|
| 36 |
+
francilien soit sur le plan géographique avec une présence significative (de départements
|
| 37 |
+
franciliens, de nombre d’adhérents). Peuvent être bénéficiaires les personnes
|
| 38 |
+
morales de droit privé ayant au moins 1 an d’existence'
|
| 39 |
+
- La Région cofinance entre 100.000€ et 120.000€ maximum des allocations de recherche
|
| 40 |
+
doctorale de 36 mois sur des projets comportant une dimension numérique
|
| 41 |
+
- source_sentence: Quel type de projets la Région Île-de-France subventionne-t-elle
|
| 42 |
+
pour valoriser le patrimoine culturel ?
|
|
|
|
|
|
|
|
|
|
|
|
|
| 43 |
sentences:
|
| 44 |
+
- 'Le dispositif est accessible à tous les OFA sous réserve de remplir les 5 conditions
|
| 45 |
+
suivantes : Dispenser une activité apprentissage ayant obtenu une certification,Dispenser
|
| 46 |
+
des formations en apprentissage sur le territoire francilien depuis au moins 1
|
| 47 |
+
an en qualité de CFA, d’OFA ou d’UFA,Présenter un projet d’investissement prévu
|
| 48 |
+
pour la dispense de formations en apprentissage sur le territoire francilien,Être
|
| 49 |
+
propriétaire du bien pour lequel une subvention est sollicitée ou titulaire d’un
|
| 50 |
+
bail récemment renouvelé (ou engagement du propriétaire à renouveler le bail),
|
| 51 |
+
en propre ou sous la forme de SCI, et assurant la maîtrise d’ouvrage des travaux
|
| 52 |
+
d’investissement,Présenter un besoin de financement sur le projet d’investissement
|
| 53 |
+
ne pouvant être pris en charge au titre des fonds propres de la structure et de
|
| 54 |
+
tiers financeurs'
|
| 55 |
+
- Jeunes scientifiques réalisant leur doctorat partagé entre un établissement d'enseignement
|
| 56 |
+
supérieur de recherche et une structure du monde socio-économique établis en Île-de-France
|
| 57 |
+
- 'Type de project: Actions de valorisation du patrimoine (expos physiques ou virtuelles,
|
| 58 |
+
journées d’étude, site Internet, publications, documentaires…),Outils de médiation (cartes
|
| 59 |
+
et itinéraires papier ou numériques, livrets de visite, multimédia, parcours d’interprétation…),Dispositifs
|
| 60 |
+
pédagogiques (mallettes pédagogiques, Moocs, supports de visite pour les jeunes…),Événements
|
| 61 |
+
avec forte dimension patrimoniale, rayonnants à l’échelle de l’Île-de-France'
|
|
|
|
|
|
|
|
|
|
|
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|
| 62 |
model-index:
|
| 63 |
- name: BGE base Financial Matryoshka
|
| 64 |
results:
|
|
|
|
| 83 |
type: BinaryClassifEval
|
| 84 |
metrics:
|
| 85 |
- type: cosine_accuracy
|
| 86 |
+
value: 0.9
|
| 87 |
name: Cosine Accuracy
|
| 88 |
- type: cosine_accuracy_threshold
|
| 89 |
+
value: 0.6570022106170654
|
| 90 |
name: Cosine Accuracy Threshold
|
| 91 |
- type: cosine_f1
|
| 92 |
+
value: 0.9473684210526316
|
| 93 |
name: Cosine F1
|
| 94 |
- type: cosine_f1_threshold
|
| 95 |
+
value: 0.6570022106170654
|
| 96 |
name: Cosine F1 Threshold
|
| 97 |
- type: cosine_precision
|
| 98 |
value: 1.0
|
| 99 |
name: Cosine Precision
|
| 100 |
- type: cosine_recall
|
| 101 |
+
value: 0.9
|
| 102 |
name: Cosine Recall
|
| 103 |
- type: cosine_ap
|
| 104 |
value: 1.0
|
|
|
|
| 159 |
model = SentenceTransformer("model")
|
| 160 |
# Run inference
|
| 161 |
sentences = [
|
| 162 |
+
'Quel type de projets la Région Île-de-France subventionne-t-elle pour valoriser le patrimoine culturel ?',
|
| 163 |
+
'Type de project: Actions de valorisation du patrimoine (expos physiques ou virtuelles, journées d’étude, site Internet, publications, documentaires…),Outils de médiation (cartes et itinéraires papier ou numériques, livrets de visite, multimédia, parcours d’interprétation…),Dispositifs pédagogiques (mallettes pédagogiques, Moocs, supports de visite pour les jeunes…),Événements avec forte dimension patrimoniale, rayonnants à l’échelle de l’Île-de-France',
|
| 164 |
+
'Le dispositif est accessible à tous les OFA sous réserve de remplir les 5 conditions suivantes : Dispenser une activité apprentissage ayant obtenu une certification,Dispenser des formations en apprentissage sur le territoire francilien depuis au moins 1 an en qualité de CFA, d’OFA ou d’UFA,Présenter un projet d’investissement prévu pour la dispense de formations en apprentissage sur le territoire francilien,Être propriétaire du bien pour lequel une subvention est sollicitée ou titulaire d’un bail récemment renouvelé (ou engagement du propriétaire à renouveler le bail), en propre ou sous la forme de SCI, et assurant la maîtrise d’ouvrage des travaux d’investissement,Présenter un besoin de financement sur le projet d’investissement ne pouvant être pris en charge au titre des fonds propres de la structure et de tiers financeurs',
|
| 165 |
]
|
| 166 |
embeddings = model.encode(sentences)
|
| 167 |
print(embeddings.shape)
|
|
|
|
| 218 |
|
| 219 |
| Metric | Value |
|
| 220 |
|:--------------------------|:--------|
|
| 221 |
+
| cosine_accuracy | 0.9 |
|
| 222 |
+
| cosine_accuracy_threshold | 0.657 |
|
| 223 |
+
| cosine_f1 | 0.9474 |
|
| 224 |
+
| cosine_f1_threshold | 0.657 |
|
| 225 |
| cosine_precision | 1.0 |
|
| 226 |
+
| cosine_recall | 0.9 |
|
| 227 |
| **cosine_ap** | **1.0** |
|
| 228 |
| cosine_mcc | 0.0 |
|
| 229 |
|
|
|
|
| 249 |
* Size: 3,696 training samples
|
| 250 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| 251 |
* Approximate statistics based on the first 1000 samples:
|
| 252 |
+
| | sentence1 | sentence2 | label |
|
| 253 |
+
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
|
| 254 |
+
| type | string | string | int |
|
| 255 |
+
| details | <ul><li>min: 33 tokens</li><li>mean: 39.4 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 49 tokens</li><li>mean: 98.8 tokens</li><li>max: 240 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
|
| 256 |
* Samples:
|
| 257 |
| sentence1 | sentence2 | label |
|
| 258 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
|
|
|
| 275 |
* Size: 687 evaluation samples
|
| 276 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| 277 |
* Approximate statistics based on the first 687 samples:
|
| 278 |
+
| | sentence1 | sentence2 | label |
|
| 279 |
+
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
|
| 280 |
+
| type | string | string | int |
|
| 281 |
+
| details | <ul><li>min: 20 tokens</li><li>mean: 32.7 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 90.0 tokens</li><li>max: 257 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
|
| 282 |
* Samples:
|
| 283 |
| sentence1 | sentence2 | label |
|
| 284 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
|
|
|
| 299 |
- `eval_strategy`: epoch
|
| 300 |
- `per_device_train_batch_size`: 2
|
| 301 |
- `per_device_eval_batch_size`: 2
|
| 302 |
+
- `num_train_epochs`: 1
|
| 303 |
- `lr_scheduler_type`: cosine
|
| 304 |
- `warmup_ratio`: 0.1
|
| 305 |
- `bf16`: True
|
|
|
|
| 328 |
- `adam_beta2`: 0.999
|
| 329 |
- `adam_epsilon`: 1e-08
|
| 330 |
- `max_grad_norm`: 1.0
|
| 331 |
+
- `num_train_epochs`: 1
|
| 332 |
- `max_steps`: -1
|
| 333 |
- `lr_scheduler_type`: cosine
|
| 334 |
- `lr_scheduler_kwargs`: {}
|
|
|
|
| 430 |
</details>
|
| 431 |
|
| 432 |
### Training Logs
|
| 433 |
+
| Epoch | Step | Validation Loss | EmbeddingSimEval_spearman_cosine | BinaryClassifEval_cosine_ap |
|
| 434 |
+
|:-------:|:-----:|:---------------:|:--------------------------------:|:---------------------------:|
|
| 435 |
+
| **1.0** | **5** | **0.3948** | **nan** | **1.0** |
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| 436 |
|
| 437 |
* The bold row denotes the saved checkpoint.
|
|
|
|
| 438 |
|
| 439 |
### Framework Versions
|
| 440 |
- Python: 3.11.9
|
eval/binary_classification_evaluation_BinaryClassifEval_results.csv
CHANGED
|
@@ -1,12 +1,3 @@
|
|
| 1 |
epoch,steps,cosine_accuracy,cosine_accuracy_threshold,cosine_f1,cosine_precision,cosine_recall,cosine_f1_threshold,cosine_ap,cosine_mcc
|
| 2 |
-
1.0,
|
| 3 |
-
|
| 4 |
-
3.0,5544,0.9985443959243085,-0.23389852046966553,0.9992716678805535,1.0,0.9985443959243085,-0.23389852046966553,1.0,0.0
|
| 5 |
-
4.0,7392,0.9985443959243085,-0.1840038299560547,0.9992716678805535,1.0,0.9985443959243085,-0.1840038299560547,1.0,0.0
|
| 6 |
-
5.0,9240,0.9985443959243085,-0.13899779319763184,0.9992716678805535,1.0,0.9985443959243085,-0.13899779319763184,1.0,0.0
|
| 7 |
-
6.0,11088,0.9985443959243085,-0.2695996165275574,0.9992716678805535,1.0,0.9985443959243085,-0.2695996165275574,1.0,0.0
|
| 8 |
-
7.0,12936,0.9985443959243085,-0.08752036094665527,0.9992716678805535,1.0,0.9985443959243085,-0.08752036094665527,1.0,0.0
|
| 9 |
-
8.0,14784,0.9985443959243085,-0.2031029462814331,0.9992716678805535,1.0,0.9985443959243085,-0.2031029462814331,1.0,0.0
|
| 10 |
-
9.0,16632,0.9985443959243085,-0.18599945306777954,0.9992716678805535,1.0,0.9985443959243085,-0.18599945306777954,1.0,0.0
|
| 11 |
-
10.0,18480,0.9985443959243085,-0.1892727017402649,0.9992716678805535,1.0,0.9985443959243085,-0.1892727017402649,1.0,0.0
|
| 12 |
-
10.0,18480,0.9985443959243085,-0.1892727017402649,0.9992716678805535,1.0,0.9985443959243085,-0.1892727017402649,1.0,0.0
|
|
|
|
| 1 |
epoch,steps,cosine_accuracy,cosine_accuracy_threshold,cosine_f1,cosine_precision,cosine_recall,cosine_f1_threshold,cosine_ap,cosine_mcc
|
| 2 |
+
1.0,5,0.9,0.6570022106170654,0.9473684210526316,1.0,0.9,0.6570022106170654,1.0,0.0
|
| 3 |
+
1.0,5,0.9,0.6570022106170654,0.9473684210526316,1.0,0.9,0.6570022106170654,1.0,0.0
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
eval/similarity_evaluation_EmbeddingSimEval_results.csv
CHANGED
|
@@ -1,12 +1,3 @@
|
|
| 1 |
epoch,steps,cosine_pearson,cosine_spearman
|
| 2 |
-
1.0,
|
| 3 |
-
|
| 4 |
-
3.0,5544,nan,nan
|
| 5 |
-
4.0,7392,nan,nan
|
| 6 |
-
5.0,9240,nan,nan
|
| 7 |
-
6.0,11088,nan,nan
|
| 8 |
-
7.0,12936,nan,nan
|
| 9 |
-
8.0,14784,nan,nan
|
| 10 |
-
9.0,16632,nan,nan
|
| 11 |
-
10.0,18480,nan,nan
|
| 12 |
-
10.0,18480,nan,nan
|
|
|
|
| 1 |
epoch,steps,cosine_pearson,cosine_spearman
|
| 2 |
+
1.0,5,nan,nan
|
| 3 |
+
1.0,5,nan,nan
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 437951328
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6c7739fb0494804cc14ea6c89aa4b9a45a3f4a8ef95a4a701a86cf23286699ba
|
| 3 |
size 437951328
|
runs/Feb25_10-51-33_algo-1/events.out.tfevents.1740480693.algo-1.62.0
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8bf0de235a639bdf826e588c54023dad9bb608b29054fe23b803b6351656c430
|
| 3 |
+
size 5796
|
runs/Feb25_10-51-33_algo-1/events.out.tfevents.1740480710.algo-1.62.1
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:458a74368cd233453983bc8fe9fb6971a24667f0977743082e7b22477d16f553
|
| 3 |
+
size 1166
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5624
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bf3cb4c02a5099438e136fcc7b6a799e8c7f467468dd4e3c86b93157c90eea41
|
| 3 |
size 5624
|