sentence-transformers safetensors modernbert sentence-similarity feature-extraction generated_from_trainer dataset_size:47560 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss fr arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:nomic-ai/modernbert-embed-base base_model:finetune:nomic-ai/modernbert-embed-base license:apache-2.0 model-index co2_eq_emissions autotrain_compatible endpoints_compatible region:us
---
base_model: nomic-ai/modernbert-embed-base
language:
- fr
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_ndcg@15
- cosine_ndcg@20
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:47560
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Pourquoi l'enfant de Jéroboam sera-t-il le seul de sa maison à
    être enterré?
  sentences:
  - Nathan le prophète.
  - Parce qu'il est le seul de la maison de Jéroboam en qui se soit trouvé quelque
    chose de bon devant l'Éternel, le Dieu d'Israël.
  - Deux ans.
- source_sentence: Que dit le texte sur la foi capable de transporter des montagnes
    sans charité?
  sentences:
  - Urie était un Héthien.
  - Il dit que même avec une foi capable de transporter des montagnes, sans la charité,
    cela ne vaut rien.
  - David est allé se présenter devant l'Éternel et a exprimé son humilité et sa gratitude
    envers Dieu.
- source_sentence: Quels sont les noms des fils de Schobal?
  sentences:
  - Reaja, Jachath, Achumaï et Lahad.
  - Le côté du midi échut à Obed-Édom, et la maison des magasins à ses fils.
  - Meschélémia avait dix-huit fils et frères vaillants.
- source_sentence: Qui a succédé au roi Asa après sa mort?
  sentences:
  - 'L''un dit: Moi, je suis de Paul! Et un autre: Moi, d''Apollos!'
  - 'Neuf fils: Zemira, Joasch, Éliézer, Éljoénaï, Omri, Jerémoth, Abija, Anathoth
    et Alameth, enregistrés au nombre de vingt mille deux cents.'
  - Josaphat, son fils.
- source_sentence: Quelles tâches les Lévites devaient-ils accomplir dans le service
    de la maison de l'Éternel?
  sentences:
  - Ils devaient prendre soin des parvis et des chambres, purifier toutes les choses
    saintes, s'occuper des pains de proposition, de la fleur de farine pour les offrandes,
    des galettes sans levain, des gâteaux cuits sur la plaque et des gâteaux frits,
    et de toutes les mesures de capacité et de longueur.
  - Les chefs des maisons paternelles, les chefs des tribus d'Israël, les chefs de
    milliers et de centaines, et les intendants du roi.
  - Les enfants sont considérés comme saints.
co2_eq_emissions:
  emissions: 11.494424944753328
  energy_consumed: 0.20511474053343792
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
  ram_total_size: 7.6847381591796875
  hours_used: 6.806
  hardware_used: 1 x NVIDIA GeForce GTX 1660 Ti
model-index:
- name: modernbert-embed-base-bible
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.17498667614141056
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.24835672410730147
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2762480014212116
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.320305560490318
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17498667614141056
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08278557470243382
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05524960028424231
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0320305560490318
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17498667614141056
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.24835672410730147
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2762480014212116
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.320305560490318
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.24430049048684818
      name: Cosine Ndcg@10
    - type: cosine_ndcg@15
      value: 0.2525347835304927
      name: Cosine Ndcg@15
    - type: cosine_ndcg@20
      value: 0.2574496509992833
      name: Cosine Ndcg@20
    - type: cosine_mrr@10
      value: 0.2204687601338871
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22764969395073778
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.17161129863208385
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.24018475750577367
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2719843666725884
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.31621957718955407
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17161129863208385
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08006158583525788
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05439687333451768
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03162195771895541
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17161129863208385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.24018475750577367
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2719843666725884
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.31621957718955407
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.23947113373513576
      name: Cosine Ndcg@10
    - type: cosine_ndcg@15
      value: 0.24636222462199156
      name: Cosine Ndcg@15
    - type: cosine_ndcg@20
      value: 0.2517242130957284
      name: Cosine Ndcg@20
    - type: cosine_mrr@10
      value: 0.2154852845384024
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2225725360678114
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.16024160596908865
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22757150470776336
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2602593711138746
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3075146562444484
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.16024160596908865
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07585716823592112
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.052051874222774915
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.030751465624444838
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16024160596908865
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22757150470776336
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2602593711138746
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3075146562444484
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.22844579790475078
      name: Cosine Ndcg@10
    - type: cosine_ndcg@15
      value: 0.2357050364715922
      name: Cosine Ndcg@15
    - type: cosine_ndcg@20
      value: 0.24051535612507915
      name: Cosine Ndcg@20
    - type: cosine_mrr@10
      value: 0.20381231547513284
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21077486383464478
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.14372002131817374
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20465446793391368
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.23307869959140168
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.279445727482679
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14372002131817374
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06821815597797122
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04661573991828033
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0279445727482679
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14372002131817374
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20465446793391368
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.23307869959140168
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.279445727482679
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20572968417646773
      name: Cosine Ndcg@10
    - type: cosine_ndcg@15
      value: 0.21411686675503838
      name: Cosine Ndcg@15
    - type: cosine_ndcg@20
      value: 0.21935674398662894
      name: Cosine Ndcg@20
    - type: cosine_mrr@10
      value: 0.1828928000406064
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.19012440317942259
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.11067685201634393
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.15953100017765146
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.18617871735654645
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.22721620181204477
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11067685201634393
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05317700005921715
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03723574347130929
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.022721620181204476
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11067685201634393
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15953100017765146
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18617871735654645
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.22721620181204477
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16327341570689552
      name: Cosine Ndcg@10
    - type: cosine_ndcg@15
      value: 0.1699977455983759
      name: Cosine Ndcg@15
    - type: cosine_ndcg@20
      value: 0.17462327712912765
      name: Cosine Ndcg@20
    - type: cosine_mrr@10
      value: 0.1435284115422685
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1500325081763102
      name: Cosine Map@100
---

# modernbert-embed-base-bible

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision bb0033c9f3def40c3c5b26ff0b53c74f3320d703 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **Language:** fr
- **License:** apache-2.0

### 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (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("Steve77/modernbert-embed-base-bible")
# Run inference
sentences = [
    "Quelles tâches les Lévites devaient-ils accomplir dans le service de la maison de l'Éternel?",
    "Ils devaient prendre soin des parvis et des chambres, purifier toutes les choses saintes, s'occuper des pains de proposition, de la fleur de farine pour les offrandes, des galettes sans levain, des gâteaux cuits sur la plaque et des gâteaux frits, et de toutes les mesures de capacité et de longueur.",
    "Les chefs des maisons paternelles, les chefs des tribus d'Israël, les chefs de milliers et de centaines, et les intendants du roi.",
]
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)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1   | 0.175      | 0.1716     | 0.1602     | 0.1437     | 0.1107     |
| cosine_accuracy@3   | 0.2484     | 0.2402     | 0.2276     | 0.2047     | 0.1595     |
| cosine_accuracy@5   | 0.2762     | 0.272      | 0.2603     | 0.2331     | 0.1862     |
| cosine_accuracy@10  | 0.3203     | 0.3162     | 0.3075     | 0.2794     | 0.2272     |
| cosine_precision@1  | 0.175      | 0.1716     | 0.1602     | 0.1437     | 0.1107     |
| cosine_precision@3  | 0.0828     | 0.0801     | 0.0759     | 0.0682     | 0.0532     |
| cosine_precision@5  | 0.0552     | 0.0544     | 0.0521     | 0.0466     | 0.0372     |
| cosine_precision@10 | 0.032      | 0.0316     | 0.0308     | 0.0279     | 0.0227     |
| cosine_recall@1     | 0.175      | 0.1716     | 0.1602     | 0.1437     | 0.1107     |
| cosine_recall@3     | 0.2484     | 0.2402     | 0.2276     | 0.2047     | 0.1595     |
| cosine_recall@5     | 0.2762     | 0.272      | 0.2603     | 0.2331     | 0.1862     |
| cosine_recall@10    | 0.3203     | 0.3162     | 0.3075     | 0.2794     | 0.2272     |
| cosine_ndcg@10      | 0.2443     | 0.2395     | 0.2284     | 0.2057     | 0.1633     |
| cosine_ndcg@15      | 0.2525     | 0.2464     | 0.2357     | 0.2141     | 0.17       |
| **cosine_ndcg@20**  | **0.2574** | **0.2517** | **0.2405** | **0.2194** | **0.1746** |
| cosine_mrr@10       | 0.2205     | 0.2155     | 0.2038     | 0.1829     | 0.1435     |
| cosine_map@100      | 0.2276     | 0.2226     | 0.2108     | 0.1901     | 0.15       |

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### Recommendations

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 47,560 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 8 tokens</li><li>mean: 21.11 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 24.84 tokens</li><li>max: 108 tokens</li></ul> |
* Samples:
  | anchor                                                | positive                                            |
  |:------------------------------------------------------|:----------------------------------------------------|
  | <code>Quels sont les noms des fils de Schobal?</code> | <code>Aljan, Manahath, Ébal, Schephi et Onam</code> |
  | <code>Quels sont les noms des fils de Tsibeon?</code> | <code>Ajja et Ana</code>                            |
  | <code>Qui est le fils d'Ana?</code>                   | <code>Dischon</code>                                |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `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`: None
- `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`: None
- `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
| Epoch      | Step    | Training Loss | dim_768_cosine_ndcg@20 | dim_512_cosine_ndcg@20 | dim_256_cosine_ndcg@20 | dim_128_cosine_ndcg@20 | dim_64_cosine_ndcg@20 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.0538     | 10      | 12.274        | -                      | -                      | -                      | -                      | -                     |
| 0.1076     | 20      | 11.5084       | -                      | -                      | -                      | -                      | -                     |
| 0.1615     | 30      | 10.5276       | -                      | -                      | -                      | -                      | -                     |
| 0.2153     | 40      | 9.0432        | -                      | -                      | -                      | -                      | -                     |
| 0.2691     | 50      | 7.572         | -                      | -                      | -                      | -                      | -                     |
| 0.3229     | 60      | 7.7696        | -                      | -                      | -                      | -                      | -                     |
| 0.3767     | 70      | 6.5673        | -                      | -                      | -                      | -                      | -                     |
| 0.4305     | 80      | 6.6586        | -                      | -                      | -                      | -                      | -                     |
| 0.4844     | 90      | 5.5276        | -                      | -                      | -                      | -                      | -                     |
| 0.5382     | 100     | 5.9891        | -                      | -                      | -                      | -                      | -                     |
| 0.5920     | 110     | 5.2983        | -                      | -                      | -                      | -                      | -                     |
| 0.6458     | 120     | 5.6242        | -                      | -                      | -                      | -                      | -                     |
| 0.6996     | 130     | 5.498         | -                      | -                      | -                      | -                      | -                     |
| 0.7534     | 140     | 4.4201        | -                      | -                      | -                      | -                      | -                     |
| 0.8073     | 150     | 4.3818        | -                      | -                      | -                      | -                      | -                     |
| 0.8611     | 160     | 4.2175        | -                      | -                      | -                      | -                      | -                     |
| 0.9149     | 170     | 4.2341        | -                      | -                      | -                      | -                      | -                     |
| 0.9687     | 180     | 4.3349        | -                      | -                      | -                      | -                      | -                     |
| 0.9956     | 185     | -             | 0.2664                 | 0.2607                 | 0.2508                 | 0.2263                 | 0.1796                |
| 1.0269     | 190     | 4.6803        | -                      | -                      | -                      | -                      | -                     |
| 1.0807     | 200     | 3.877         | -                      | -                      | -                      | -                      | -                     |
| 1.1345     | 210     | 4.0309        | -                      | -                      | -                      | -                      | -                     |
| 1.1884     | 220     | 4.0755        | -                      | -                      | -                      | -                      | -                     |
| 1.2422     | 230     | 3.9068        | -                      | -                      | -                      | -                      | -                     |
| 1.2960     | 240     | 4.188         | -                      | -                      | -                      | -                      | -                     |
| 1.3498     | 250     | 4.3417        | -                      | -                      | -                      | -                      | -                     |
| 1.4036     | 260     | 4.0526        | -                      | -                      | -                      | -                      | -                     |
| 1.4575     | 270     | 3.3933        | -                      | -                      | -                      | -                      | -                     |
| 1.5113     | 280     | 3.8309        | -                      | -                      | -                      | -                      | -                     |
| 1.5651     | 290     | 3.5633        | -                      | -                      | -                      | -                      | -                     |
| 1.6189     | 300     | 3.8179        | -                      | -                      | -                      | -                      | -                     |
| 1.6727     | 310     | 4.0671        | -                      | -                      | -                      | -                      | -                     |
| 1.7265     | 320     | 3.3919        | -                      | -                      | -                      | -                      | -                     |
| 1.7804     | 330     | 2.6578        | -                      | -                      | -                      | -                      | -                     |
| 1.8342     | 340     | 2.6953        | -                      | -                      | -                      | -                      | -                     |
| 1.8880     | 350     | 2.8858        | -                      | -                      | -                      | -                      | -                     |
| 1.9418     | 360     | 2.8933        | -                      | -                      | -                      | -                      | -                     |
| **1.9956** | **370** | **2.9603**    | **0.2775**             | **0.2737**             | **0.2637**             | **0.2402**             | **0.1916**            |
| 2.0538     | 380     | 3.3361        | -                      | -                      | -                      | -                      | -                     |
| 2.1076     | 390     | 2.7904        | -                      | -                      | -                      | -                      | -                     |
| 2.1615     | 400     | 3.0108        | -                      | -                      | -                      | -                      | -                     |
| 2.2153     | 410     | 2.8917        | -                      | -                      | -                      | -                      | -                     |
| 2.2691     | 420     | 3.0295        | -                      | -                      | -                      | -                      | -                     |
| 2.3229     | 430     | 3.5609        | -                      | -                      | -                      | -                      | -                     |
| 2.3767     | 440     | 2.7722        | -                      | -                      | -                      | -                      | -                     |
| 2.4305     | 450     | 3.2115        | -                      | -                      | -                      | -                      | -                     |
| 2.4844     | 460     | 2.6333        | -                      | -                      | -                      | -                      | -                     |
| 2.5382     | 470     | 3.2503        | -                      | -                      | -                      | -                      | -                     |
| 2.5920     | 480     | 2.7708        | -                      | -                      | -                      | -                      | -                     |
| 2.6458     | 490     | 3.167         | -                      | -                      | -                      | -                      | -                     |
| 2.6996     | 500     | 3.1447        | -                      | -                      | -                      | -                      | -                     |
| 2.7534     | 510     | 2.0428        | -                      | -                      | -                      | -                      | -                     |
| 2.8073     | 520     | 2.0001        | -                      | -                      | -                      | -                      | -                     |
| 2.8611     | 530     | 2.0826        | -                      | -                      | -                      | -                      | -                     |
| 2.9149     | 540     | 2.0853        | -                      | -                      | -                      | -                      | -                     |
| 2.9687     | 550     | 2.2365        | -                      | -                      | -                      | -                      | -                     |
| 2.9956     | 555     | -             | 0.2660                 | 0.2604                 | 0.2509                 | 0.2266                 | 0.1810                |
| 3.0269     | 560     | 2.762         | -                      | -                      | -                      | -                      | -                     |
| 3.0807     | 570     | 2.1219        | -                      | -                      | -                      | -                      | -                     |
| 3.1345     | 580     | 2.2908        | -                      | -                      | -                      | -                      | -                     |
| 3.1884     | 590     | 2.6195        | -                      | -                      | -                      | -                      | -                     |
| 3.2422     | 600     | 2.3468        | -                      | -                      | -                      | -                      | -                     |
| 3.2960     | 610     | 2.7504        | -                      | -                      | -                      | -                      | -                     |
| 3.3498     | 620     | 2.9486        | -                      | -                      | -                      | -                      | -                     |
| 3.4036     | 630     | 2.7281        | -                      | -                      | -                      | -                      | -                     |
| 3.4575     | 640     | 2.188         | -                      | -                      | -                      | -                      | -                     |
| 3.5113     | 650     | 2.5494        | -                      | -                      | -                      | -                      | -                     |
| 3.5651     | 660     | 2.426         | -                      | -                      | -                      | -                      | -                     |
| 3.6189     | 670     | 2.6478        | -                      | -                      | -                      | -                      | -                     |
| 3.6727     | 680     | 2.9209        | -                      | -                      | -                      | -                      | -                     |
| 3.7265     | 690     | 2.3512        | -                      | -                      | -                      | -                      | -                     |
| 3.7804     | 700     | 1.6746        | -                      | -                      | -                      | -                      | -                     |
| 3.8342     | 710     | 1.739         | -                      | -                      | -                      | -                      | -                     |
| 3.8880     | 720     | 1.951         | -                      | -                      | -                      | -                      | -                     |
| 3.9418     | 730     | 1.9886        | -                      | -                      | -                      | -                      | -                     |
| 3.9956     | 740     | 2.1022        | 0.2574                 | 0.2517                 | 0.2405                 | 0.2194                 | 0.1746                |

* The bold row denotes the saved checkpoint.

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.205 kWh
- **Carbon Emitted**: 0.011 kg of CO2
- **Hours Used**: 6.806 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce GTX 1660 Ti
- **CPU Model**: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
- **RAM Size**: 7.68 GB

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1
- Accelerate: 1.2.1
- Datasets: 2.19.1
- 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",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### 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}
}
```

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