sentence-transformers safetensors bert sentence-similarity feature-extraction dataset_size:1K<n<10K loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
language:
- en
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
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Żywot św. Stanisława
  sentences:
  - czym różni się Żywot św. Stanisława od Legendy św. Stanisława?
  - kto uczył malarstwa olimpijczyka Bronisława Czecha?
  - St. Louis Eagles
- source_sentence: Jaakow Jicchak Szapira
  sentences:
  - czym jest Kompas Sztuki?
  - z czego wykonana jest rzeźba Robotnik i kołchoźnica?
  - podczas którego soboru zostało ogłoszone chalcedońskie wyznanie wiary?
- source_sentence: Chłopiec z Nariokotome
  sentences:
  - ile wynosiła objętość mózgu chłopca z Nariokotome?
  - jaki pomnik odsłonięto we Lwowie 3 lipca 2011 roku?
  - Voyager 2 Voyager Golden Record Pale Blue Dot
- source_sentence: skąd pochodzi wino cirò?
  sentences:
  - skąd pochodzi nazwa Kotylniczy Wierch?
  - do czego współcześnie wykorzystuje się papier amate?
  - erystyka sofizmat błędy logiczno-językowe onus probandi
- source_sentence: Sen o zastrzyku Irmy
  sentences:
  - gdzie Freud spotkał Irmę we śnie o zastrzyku Irmy?
  - ile razy Srebrna Biblia była przywożona do Szwecji?
  - Voyager 2 Voyager Golden Record Pale Blue Dot
model-index:
- name: all-MiniLM-L6-v2-klej-dyk-v0.1
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy@1
      value: 0.19951923076923078
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.43028846153846156
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5384615384615384
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6225961538461539
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.19951923076923078
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.14342948717948717
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10769230769230768
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06225961538461538
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.19951923076923078
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.43028846153846156
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5384615384615384
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6225961538461539
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4067615454626299
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3376678876678877
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3451711286911671
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.18509615384615385
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.41346153846153844
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5096153846153846
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6033653846153846
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18509615384615385
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1378205128205128
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10192307692307692
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06033653846153846
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18509615384615385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.41346153846153844
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5096153846153846
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6033653846153846
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.39112028533472887
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.32341746794871795
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3303671597529028
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.18028846153846154
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.35336538461538464
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4423076923076923
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5192307692307693
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18028846153846154
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11778846153846154
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08846153846153845
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05192307692307692
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18028846153846154
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.35336538461538464
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4423076923076923
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5192307692307693
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3443315125767603
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2888621794871794
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2960334956693037
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.13701923076923078
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2644230769230769
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32211538461538464
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3798076923076923
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.13701923076923078
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08814102564102563
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06442307692307693
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03798076923076923
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13701923076923078
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2644230769230769
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32211538461538464
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3798076923076923
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2529381675019326
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21289396367521363
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2208612925846397
      name: Cosine Map@100
---

# all-MiniLM-L6-v2-klej-dyk-v0.1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Sen o zastrzyku Irmy',
    'gdzie Freud spotkał Irmę we śnie o zastrzyku Irmy?',
    'ile razy Srebrna Biblia była przywożona do Szwecji?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1995     |
| cosine_accuracy@3   | 0.4303     |
| cosine_accuracy@5   | 0.5385     |
| cosine_accuracy@10  | 0.6226     |
| cosine_precision@1  | 0.1995     |
| cosine_precision@3  | 0.1434     |
| cosine_precision@5  | 0.1077     |
| cosine_precision@10 | 0.0623     |
| cosine_recall@1     | 0.1995     |
| cosine_recall@3     | 0.4303     |
| cosine_recall@5     | 0.5385     |
| cosine_recall@10    | 0.6226     |
| cosine_ndcg@10      | 0.4068     |
| cosine_mrr@10       | 0.3377     |
| **cosine_map@100**  | **0.3452** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1851     |
| cosine_accuracy@3   | 0.4135     |
| cosine_accuracy@5   | 0.5096     |
| cosine_accuracy@10  | 0.6034     |
| cosine_precision@1  | 0.1851     |
| cosine_precision@3  | 0.1378     |
| cosine_precision@5  | 0.1019     |
| cosine_precision@10 | 0.0603     |
| cosine_recall@1     | 0.1851     |
| cosine_recall@3     | 0.4135     |
| cosine_recall@5     | 0.5096     |
| cosine_recall@10    | 0.6034     |
| cosine_ndcg@10      | 0.3911     |
| cosine_mrr@10       | 0.3234     |
| **cosine_map@100**  | **0.3304** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.1803    |
| cosine_accuracy@3   | 0.3534    |
| cosine_accuracy@5   | 0.4423    |
| cosine_accuracy@10  | 0.5192    |
| cosine_precision@1  | 0.1803    |
| cosine_precision@3  | 0.1178    |
| cosine_precision@5  | 0.0885    |
| cosine_precision@10 | 0.0519    |
| cosine_recall@1     | 0.1803    |
| cosine_recall@3     | 0.3534    |
| cosine_recall@5     | 0.4423    |
| cosine_recall@10    | 0.5192    |
| cosine_ndcg@10      | 0.3443    |
| cosine_mrr@10       | 0.2889    |
| **cosine_map@100**  | **0.296** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.137      |
| cosine_accuracy@3   | 0.2644     |
| cosine_accuracy@5   | 0.3221     |
| cosine_accuracy@10  | 0.3798     |
| cosine_precision@1  | 0.137      |
| cosine_precision@3  | 0.0881     |
| cosine_precision@5  | 0.0644     |
| cosine_precision@10 | 0.038      |
| cosine_recall@1     | 0.137      |
| cosine_recall@3     | 0.2644     |
| cosine_recall@5     | 0.3221     |
| cosine_recall@10    | 0.3798     |
| cosine_ndcg@10      | 0.2529     |
| cosine_mrr@10       | 0.2129     |
| **cosine_map@100**  | **0.2209** |

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## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 3,738 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                           | anchor                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 7 tokens</li><li>mean: 87.54 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 30.98 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | anchor                                                                                                                      |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | <code>Zespół Blaua (zespół Jabsa, ang. Blau syndrome, BS) – rzadka choroba genetyczna o dziedziczeniu autosomalnym dominującym, charakteryzująca się ziarniniakowym zapaleniem stawów o wczesnym początku, zapaleniem jagodówki (uveitis) i wysypką skórną, a także kamptodaktylią.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>jakie choroby genetyczne dziedziczą się autosomalnie dominująco?</code>                                               |
  | <code>Gorgippia Gorgippia – starożytne miasto bosporańskie nad Morzem Czarnym, którego pozostałości znajdują się obecnie pod współczesną zabudową centralnej części miasta Anapa w Kraju Krasnodarskim w Rosji.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>gdzie obecnie znajduje się starożytne miasto Gorgippia?</code>                                                        |
  | <code>Ulubionym dystansem Rücker było 400 metrów i to na nim notowała największe indywidualne sukcesy : srebrny medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) 6. miejsce w Pucharze Świata w Lekkoatletyce (Hawana 1992) 5. miejsce na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) srebro podczas Mistrzostw Świata w Lekkoatletyce (Sewilla 1999) złota medalistka mistrzostw Niemiec Duże sukcesy odnosiła także w sztafecie 4 x 400 metrów : złoto Mistrzostw Europy juniorów w lekkoatletyce (Varaždin 1989) złoty medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) brąz na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) brązowy medal podczas Igrzysk Olimpijskich (Atlanta 1996) brąz na Halowych Mistrzostwach Świata w Lekkoatletyce (Paryż 1997) złoto Mistrzostw Świata w Lekkoatletyce (Ateny 1997) brązowy medal Mistrzostw Świata w Lekkoatletyce (Sewilla 1999)</code> | <code>kto zaprojektował medale, które będą wręczane podczas tegorocznych mistrzostw Europy juniorów w lekkoatletyce?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: 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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 32
- `eval_accumulation_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`: 5
- `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`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0          | 0      | -             | 0.1945                 | 0.2243                 | 0.2302                 | 0.1499                |
| 0.2735     | 1      | 8.2585        | -                      | -                      | -                      | -                     |
| 0.5470     | 2      | 8.4215        | -                      | -                      | -                      | -                     |
| 0.8205     | 3      | 7.899         | 0.2205                 | 0.2510                 | 0.2597                 | 0.1677                |
| 1.0855     | 4      | 6.5734        | -                      | -                      | -                      | -                     |
| 1.3590     | 5      | 6.2406        | -                      | -                      | -                      | -                     |
| 1.6325     | 6      | 6.0949        | -                      | -                      | -                      | -                     |
| 1.9060     | 7      | 5.7149        | 0.2736                 | 0.3061                 | 0.3224                 | 0.2124                |
| 2.1709     | 8      | 5.153         | -                      | -                      | -                      | -                     |
| 2.4444     | 9      | 5.3615        | -                      | -                      | -                      | -                     |
| 2.7179     | 10     | 5.3069        | -                      | -                      | -                      | -                     |
| 2.9915     | 11     | 5.1567        | 0.2914                 | 0.3238                 | 0.3402                 | 0.2191                |
| 3.2564     | 12     | 4.6824        | -                      | -                      | -                      | -                     |
| 3.5299     | 13     | 5.1072        | -                      | -                      | -                      | -                     |
| **3.8034** | **14** | **5.1575**    | **0.2967**             | **0.3302**             | **0.3443**             | **0.2196**            |
| 4.0684     | 15     | 4.5651        | 0.2960                 | 0.3304                 | 0.3452                 | 0.2209                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate: 0.27.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## 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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