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:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
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: Herkules na rozstajach
  sentences:
  - jak zinterpretować wymowę obrazu Herkules na rozstajach?
  - w jakim celu nowożeńcom w Korei wręcza się injeolmi?
  - z jakiego powodu zwołano synod w Whitby?
- source_sentence: gdzie rośnie bokkonia?
  sentences:
  - gdzie występuje rogownica szerokolistna?
  - Dłutowanie metodą Maaga Struganie metodą Sunderlanda
  - kim byli beatyfikowani przez papieża Jana Pawła II męczennicy z Almerii?
- source_sentence: kto walczył o Brisbane?
  sentences:
  - Szczurza gorączka TAM Gorączka od ugryzienia szczura
  - Szczurza gorączka TAM Gorączka od ugryzienia szczura
  - który nadworny fotograf sprzedał swój patent firmie Eastman Kodak?
- source_sentence: Morskie Oko (kabaret)
  sentences:
  - jak skończył się spór o Morskie Oko?
  - ile razy Srebrna Biblia była przywożona do Szwecji?
  - W latach 1955–1956 część więźniów przebywających w Spassku zwolniono.
- source_sentence: ile katod ma duodioda?
  sentences:
  - kto nosi mantyle?
  - w jakim celu nowożeńcom w Korei wręcza się injeolmi?
  - W latach 1955–1956 część więźniów przebywających w Spassku zwolniono.
model-index:
- name: bge-base-en-v1.5-klej-dyk
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.20432692307692307
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5024038461538461
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6802884615384616
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7548076923076923
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.20432692307692307
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1674679487179487
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1360576923076923
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07548076923076923
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.20432692307692307
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5024038461538461
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6802884615384616
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7548076923076923
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4741957684261531
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3839495573870572
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3909524912840153
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.19471153846153846
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.49278846153846156
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6634615384615384
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7548076923076923
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.19471153846153846
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1642628205128205
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13269230769230766
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07548076923076921
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.19471153846153846
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.49278846153846156
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6634615384615384
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7548076923076923
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4648228460121699
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.37225847069597073
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.378344181427981
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.18990384615384615
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4543269230769231
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6057692307692307
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7067307692307693
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18990384615384615
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15144230769230768
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.12115384615384615
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07067307692307692
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18990384615384615
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4543269230769231
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6057692307692307
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7067307692307693
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.437691661658994
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3522741147741148
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.35902651881139014
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.18509615384615385
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4375
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5480769230769231
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6442307692307693
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18509615384615385
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.14583333333333331
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1096153846153846
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06442307692307692
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18509615384615385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4375
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5480769230769231
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6442307692307693
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4084493303372093
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.33323508089133086
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3393128348021269
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.17307692307692307
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3389423076923077
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4254807692307692
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5144230769230769
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17307692307692307
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11298076923076923
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08509615384615386
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05144230769230769
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17307692307692307
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3389423076923077
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4254807692307692
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5144230769230769
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.333723313431585
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2768763354700855
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2853193687152632
      name: Cosine Map@100
---

# bge-base-en-v1.5-klej-dyk

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). 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 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': 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'ile katod ma duodioda?',
    'kto nosi mantyle?',
    'w jakim celu nowożeńcom w Korei wręcza się injeolmi?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.2043    |
| cosine_accuracy@3   | 0.5024    |
| cosine_accuracy@5   | 0.6803    |
| cosine_accuracy@10  | 0.7548    |
| cosine_precision@1  | 0.2043    |
| cosine_precision@3  | 0.1675    |
| cosine_precision@5  | 0.1361    |
| cosine_precision@10 | 0.0755    |
| cosine_recall@1     | 0.2043    |
| cosine_recall@3     | 0.5024    |
| cosine_recall@5     | 0.6803    |
| cosine_recall@10    | 0.7548    |
| cosine_ndcg@10      | 0.4742    |
| cosine_mrr@10       | 0.3839    |
| **cosine_map@100**  | **0.391** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1947     |
| cosine_accuracy@3   | 0.4928     |
| cosine_accuracy@5   | 0.6635     |
| cosine_accuracy@10  | 0.7548     |
| cosine_precision@1  | 0.1947     |
| cosine_precision@3  | 0.1643     |
| cosine_precision@5  | 0.1327     |
| cosine_precision@10 | 0.0755     |
| cosine_recall@1     | 0.1947     |
| cosine_recall@3     | 0.4928     |
| cosine_recall@5     | 0.6635     |
| cosine_recall@10    | 0.7548     |
| cosine_ndcg@10      | 0.4648     |
| cosine_mrr@10       | 0.3723     |
| **cosine_map@100**  | **0.3783** |

#### 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   | 0.1899    |
| cosine_accuracy@3   | 0.4543    |
| cosine_accuracy@5   | 0.6058    |
| cosine_accuracy@10  | 0.7067    |
| cosine_precision@1  | 0.1899    |
| cosine_precision@3  | 0.1514    |
| cosine_precision@5  | 0.1212    |
| cosine_precision@10 | 0.0707    |
| cosine_recall@1     | 0.1899    |
| cosine_recall@3     | 0.4543    |
| cosine_recall@5     | 0.6058    |
| cosine_recall@10    | 0.7067    |
| cosine_ndcg@10      | 0.4377    |
| cosine_mrr@10       | 0.3523    |
| **cosine_map@100**  | **0.359** |

#### 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   | 0.1851     |
| cosine_accuracy@3   | 0.4375     |
| cosine_accuracy@5   | 0.5481     |
| cosine_accuracy@10  | 0.6442     |
| cosine_precision@1  | 0.1851     |
| cosine_precision@3  | 0.1458     |
| cosine_precision@5  | 0.1096     |
| cosine_precision@10 | 0.0644     |
| cosine_recall@1     | 0.1851     |
| cosine_recall@3     | 0.4375     |
| cosine_recall@5     | 0.5481     |
| cosine_recall@10    | 0.6442     |
| cosine_ndcg@10      | 0.4084     |
| cosine_mrr@10       | 0.3332     |
| **cosine_map@100**  | **0.3393** |

#### 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   | 0.1731     |
| cosine_accuracy@3   | 0.3389     |
| cosine_accuracy@5   | 0.4255     |
| cosine_accuracy@10  | 0.5144     |
| cosine_precision@1  | 0.1731     |
| cosine_precision@3  | 0.113      |
| cosine_precision@5  | 0.0851     |
| cosine_precision@10 | 0.0514     |
| cosine_recall@1     | 0.1731     |
| cosine_recall@3     | 0.3389     |
| cosine_recall@5     | 0.4255     |
| cosine_recall@10    | 0.5144     |
| cosine_ndcg@10      | 0.3337     |
| cosine_mrr@10       | 0.2769     |
| **cosine_map@100**  | **0.2853** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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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: 6 tokens</li><li>mean: 89.95 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 30.73 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                  | anchor                                                                             |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | <code>Rynek Kolumna Matki Boskiej, tzw. Kolumna Maryjna wykonana w latach 1725-1727 przez Johanna Melchiora Österreicha.</code>                                                                                                                                                           | <code>kto jest autorem kolumny maryjnej na raciborskim rynku?</code>               |
  | <code>Chleb razowy jest ciemniejszy i zawiera większą ilość błonnika oraz składników mineralnych niż chleb biały (pytlowy, czyli wypiekany z mąki przesiewanej przez pytel), bowiem jest w nim większy udział drobin pochodzących z łupin ziarna, gdzie gromadzą się te składniki.</code> | <code>które składniki razowego chleba odpowiadają za jego walory zdrowotne?</code> |
  | <code>Najgłębsza znana studnia krasowa to jaskinia Vrtoglavica w Słowenii o głębokości ponad 600 metrów.</code>                                                                                                                                                                           | <code>ile metrów głębokości mierzy studnia na podwórzu klasztoru w Czernej?</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
- `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`: 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
- `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`: 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_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6838     | 10     | 6.5594        | -                      | -                      | -                      | -                     | -                      |
| 0.9573     | 14     | -             | 0.3319                 | 0.3751                 | 0.3955                 | 0.2618                | 0.4033                 |
| 1.3675     | 20     | 4.2206        | -                      | -                      | -                      | -                     | -                      |
| 1.9829     | 29     | -             | 0.3324                 | 0.3591                 | 0.3807                 | 0.2833                | 0.3946                 |
| 2.0513     | 30     | 3.3414        | -                      | -                      | -                      | -                     | -                      |
| 2.7350     | 40     | 2.9757        | -                      | -                      | -                      | -                     | -                      |
| 2.9402     | 43     | -             | 0.3375                 | 0.3570                 | 0.3805                 | 0.2840                | 0.3905                 |
| 3.4188     | 50     | 2.8884        | -                      | -                      | -                      | -                     | -                      |
| **3.8291** | **56** | **-**         | **0.3393**             | **0.359**              | **0.3783**             | **0.2853**            | **0.391**              |

* 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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