---
base_model: BAAI/bge-base-en-v1.5
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_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: Qui a écouté le roi Asa et envoyé son armée contre les villes d'Israël?
  sentences:
  - Ben-Hadad.
  - Il se prosterna devant le roi, le visage contre terre.
  - Baescha, fils d'Achija.
- source_sentence: Quelle est l'importance de distribuer tous ses biens aux pauvres
    sans charité?
  sentences:
  - Adina, fils de Schiza, était le chef des Rubénites.
  - Distribuer tous ses biens aux pauvres sans charité ne sert à rien.
  - L'Éternel.
- source_sentence: Qui sont les enfants du père d'Étham?
  sentences:
  - Jizreel, Jischma, Jidbasch et leur sœur Hatselelponi.
  - Chaque division comptait vingt-quatre mille hommes.
  - 'Hosa était un fils de Merari et il avait quatre fils: Schimri, Hilkija, Thebalia,
    et Zacharie.'
- source_sentence: Combien de temps Nadab, fils de Jéroboam, a-t-il régné sur Israël?
  sentences:
  - Ils sont des serviteurs par le moyen desquels les frères ont cru, selon que le
    Seigneur l'a donné à chacun.
  - 'Sept fils: Jeusch, Benjamin, Éhud, Kenaana, Zéthan, Tarsis et Achischachar, enregistrés
    au nombre de dix-sept mille deux cents.'
  - Deux ans.
- source_sentence: Quand les Lévites devaient-ils se présenter pour louer et célébrer
    l'Éternel?
  sentences:
  - Chaque matin et chaque soir.
  - Cinq mille talents d'or et dix mille talents d'argent ont été donnés.
  - Il doit demeurer circoncis.
model-index:
- name: BGE base bible test
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.13359388879019363
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.18795523183513946
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.21389234322259726
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.25102149582519095
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.13359388879019363
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06265174394504648
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04277846864451945
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0251021495825191
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13359388879019363
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.18795523183513946
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21389234322259726
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.25102149582519095
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18816833747648484
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.16858798117458645
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.17400088915411802
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.12773139101083675
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.18546811156510926
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.20572037662106946
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.24213892343222598
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12773139101083675
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.061822703855036416
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.041144075324213894
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0242138923432226
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12773139101083675
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.18546811156510926
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.20572037662106946
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.24213892343222598
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18151482198424093
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1625760305898876
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16802226648065993
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.12488896784508793
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.17463137324569195
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.19737075857168235
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.23272339669568307
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12488896784508793
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.058210457748563975
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03947415171433648
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.023272339669568307
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12488896784508793
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.17463137324569195
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.19737075857168235
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.23272339669568307
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17440736005896854
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.156282728049472
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16141647615447188
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.10943329188132883
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.15686622845976195
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.17853970509859654
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.20838514833895896
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10943329188132883
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.052288742819920644
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03570794101971931
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0208385148338959
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10943329188132883
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15686622845976195
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.17853970509859654
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.20838514833895896
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.15566336146326976
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.13917031134121227
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14405644027137798
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.08935867827322792
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.13181737431160065
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.15011547344110854
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.17694084206786284
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08935867827322792
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04393912477053354
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03002309468822171
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.01769408420678629
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08935867827322792
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.13181737431160065
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.15011547344110854
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.17694084206786284
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.13031373727839585
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11575599150656894
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.12066444582998255
      name: Cosine Map@100
---

# BGE base bible test

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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/bge-base-bible-retrieval")
# Run inference
sentences = [
    "Quand les Lévites devaient-ils se présenter pour louer et célébrer l'Éternel?",
    'Chaque matin et chaque soir.',
    "Cinq mille talents d'or et dix mille talents d'argent ont été donnés.",
]
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]
```

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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   | 0.1336     | 0.1277     | 0.1249     | 0.1094     | 0.0894     |
| cosine_accuracy@3   | 0.188      | 0.1855     | 0.1746     | 0.1569     | 0.1318     |
| cosine_accuracy@5   | 0.2139     | 0.2057     | 0.1974     | 0.1785     | 0.1501     |
| cosine_accuracy@10  | 0.251      | 0.2421     | 0.2327     | 0.2084     | 0.1769     |
| cosine_precision@1  | 0.1336     | 0.1277     | 0.1249     | 0.1094     | 0.0894     |
| cosine_precision@3  | 0.0627     | 0.0618     | 0.0582     | 0.0523     | 0.0439     |
| cosine_precision@5  | 0.0428     | 0.0411     | 0.0395     | 0.0357     | 0.03       |
| cosine_precision@10 | 0.0251     | 0.0242     | 0.0233     | 0.0208     | 0.0177     |
| cosine_recall@1     | 0.1336     | 0.1277     | 0.1249     | 0.1094     | 0.0894     |
| cosine_recall@3     | 0.188      | 0.1855     | 0.1746     | 0.1569     | 0.1318     |
| cosine_recall@5     | 0.2139     | 0.2057     | 0.1974     | 0.1785     | 0.1501     |
| cosine_recall@10    | 0.251      | 0.2421     | 0.2327     | 0.2084     | 0.1769     |
| **cosine_ndcg@10**  | **0.1882** | **0.1815** | **0.1744** | **0.1557** | **0.1303** |
| cosine_mrr@10       | 0.1686     | 0.1626     | 0.1563     | 0.1392     | 0.1158     |
| cosine_map@100      | 0.174      | 0.168      | 0.1614     | 0.1441     | 0.1207     |

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## Bias, Risks and Limitations

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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: 21.23 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 25.14 tokens</li><li>max: 110 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
- `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`: 3
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.0538     | 10      | 12.8804       | -                      | -                      | -                      | -                      | -                     |
| 0.1076     | 20      | 12.4714       | -                      | -                      | -                      | -                      | -                     |
| 0.1615     | 30      | 11.8263       | -                      | -                      | -                      | -                      | -                     |
| 0.2153     | 40      | 11.014        | -                      | -                      | -                      | -                      | -                     |
| 0.2691     | 50      | 10.1609       | -                      | -                      | -                      | -                      | -                     |
| 0.3229     | 60      | 10.6807       | -                      | -                      | -                      | -                      | -                     |
| 0.3767     | 70      | 9.3215        | -                      | -                      | -                      | -                      | -                     |
| 0.4305     | 80      | 10.3719       | -                      | -                      | -                      | -                      | -                     |
| 0.4844     | 90      | 9.4147        | -                      | -                      | -                      | -                      | -                     |
| 0.5382     | 100     | 9.5567        | -                      | -                      | -                      | -                      | -                     |
| 0.5920     | 110     | 8.7699        | -                      | -                      | -                      | -                      | -                     |
| 0.6458     | 120     | 9.0428        | -                      | -                      | -                      | -                      | -                     |
| 0.6996     | 130     | 9.0977        | -                      | -                      | -                      | -                      | -                     |
| 0.7534     | 140     | 8.0843        | -                      | -                      | -                      | -                      | -                     |
| 0.8073     | 150     | 8.1363        | -                      | -                      | -                      | -                      | -                     |
| 0.8611     | 160     | 7.5306        | -                      | -                      | -                      | -                      | -                     |
| 0.9149     | 170     | 7.7972        | -                      | -                      | -                      | -                      | -                     |
| 0.9687     | 180     | 7.9644        | -                      | -                      | -                      | -                      | -                     |
| 0.9956     | 185     | -             | 0.1917                 | 0.1879                 | 0.1784                 | 0.1583                 | 0.1268                |
| 1.0225     | 190     | 7.6124        | -                      | -                      | -                      | -                      | -                     |
| 1.0764     | 200     | 6.6315        | -                      | -                      | -                      | -                      | -                     |
| 1.1302     | 210     | 7.2313        | -                      | -                      | -                      | -                      | -                     |
| 1.1840     | 220     | 6.5394        | -                      | -                      | -                      | -                      | -                     |
| 1.2378     | 230     | 6.7843        | -                      | -                      | -                      | -                      | -                     |
| 1.2916     | 240     | 6.9276        | -                      | -                      | -                      | -                      | -                     |
| 1.3454     | 250     | 7.2281        | -                      | -                      | -                      | -                      | -                     |
| 1.3993     | 260     | 6.9158        | -                      | -                      | -                      | -                      | -                     |
| 1.4531     | 270     | 6.5158        | -                      | -                      | -                      | -                      | -                     |
| 1.5069     | 280     | 6.916         | -                      | -                      | -                      | -                      | -                     |
| 1.5607     | 290     | 6.5717        | -                      | -                      | -                      | -                      | -                     |
| 1.6145     | 300     | 6.9225        | -                      | -                      | -                      | -                      | -                     |
| 1.6683     | 310     | 7.3981        | -                      | -                      | -                      | -                      | -                     |
| 1.7222     | 320     | 6.894         | -                      | -                      | -                      | -                      | -                     |
| 1.7760     | 330     | 6.0293        | -                      | -                      | -                      | -                      | -                     |
| 1.8298     | 340     | 5.9389        | -                      | -                      | -                      | -                      | -                     |
| 1.8836     | 350     | 5.959         | -                      | -                      | -                      | -                      | -                     |
| 1.9374     | 360     | 6.4268        | -                      | -                      | -                      | -                      | -                     |
| 1.9913     | 370     | 6.7366        | -                      | -                      | -                      | -                      | -                     |
| **1.9966** | **371** | **-**         | **0.2012**             | **0.1965**             | **0.1862**             | **0.1633**             | **0.1361**            |
| 2.0451     | 380     | 5.7871        | -                      | -                      | -                      | -                      | -                     |
| 2.0989     | 390     | 5.7358        | -                      | -                      | -                      | -                      | -                     |
| 2.1527     | 400     | 6.0964        | -                      | -                      | -                      | -                      | -                     |
| 2.2065     | 410     | 5.8331        | -                      | -                      | -                      | -                      | -                     |
| 2.2603     | 420     | 5.6152        | -                      | -                      | -                      | -                      | -                     |
| 2.3142     | 430     | 6.5018        | -                      | -                      | -                      | -                      | -                     |
| 2.3680     | 440     | 5.9798        | -                      | -                      | -                      | -                      | -                     |
| 2.4218     | 450     | 6.0598        | -                      | -                      | -                      | -                      | -                     |
| 2.4756     | 460     | 5.8222        | -                      | -                      | -                      | -                      | -                     |
| 2.5294     | 470     | 6.303         | -                      | -                      | -                      | -                      | -                     |
| 2.5832     | 480     | 5.9648        | -                      | -                      | -                      | -                      | -                     |
| 2.6371     | 490     | 6.415         | -                      | -                      | -                      | -                      | -                     |
| 2.6909     | 500     | 7.084         | -                      | -                      | -                      | -                      | -                     |
| 2.7447     | 510     | 5.692         | -                      | -                      | -                      | -                      | -                     |
| 2.7985     | 520     | 5.7706        | -                      | -                      | -                      | -                      | -                     |
| 2.8523     | 530     | 5.6943        | -                      | -                      | -                      | -                      | -                     |
| 2.9062     | 540     | 5.6817        | -                      | -                      | -                      | -                      | -                     |
| 2.9600     | 550     | 6.1265        | -                      | -                      | -                      | -                      | -                     |
| 2.9869     | 555     | -             | 0.1882                 | 0.1815                 | 0.1744                 | 0.1557                 | 0.1303                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.45.2
- PyTorch: 2.5.1
- Accelerate: 1.2.1
- Datasets: 2.19.1
- Tokenizers: 0.20.1

## 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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