sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:198 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:intfloat/multilingual-e5-large base_model:finetune:intfloat/multilingual-e5-large model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: intfloat/multilingual-e5-large
library_name: sentence-transformers
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:198
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Najčešći tipovi uključuju iznad/ispod 2.5, ukupno golova, i klađenje
    na broj golova u poluvremenima.
  sentences:
  - Koji su najčešći tipovi klađenja na golove?
  - Koje kladionice u Srbiji nude DNB opciju?
  - Šta je hendikep klađenje?
- source_sentence: Facebook grupe posvećene klađenju omogućavaju korisnicima da dobijaju
    savete i predloge od velikih zajednica korisnika i kladioničara.
  sentences:
  - Šta je limit u klađenju?
  - Kako se koristi Facebook za klađenje?
  - Šta je cash-out opcija u uživo klađenju?
- source_sentence: Najčešći tipovi uključuju klađenje na konačan ishod, broj gemova,
    broj setova, i klađenje uživo.
  sentences:
  - Koje su prednosti praćenja utakmica uživo?
  - Koji su najčešći tipovi klađenja na tenis?
  - Šta je e-novčanik?
- source_sentence: Premijum provizija je dodatna naknada koju berze kvota mogu naplatiti
    igračima za specifične usluge ili dobitke.
  sentences:
  - Šta je premijum provizija?
  - Koje su strategije za uspešno uživo klađenje?
  - Kako funkcioniše klađenje na ukupan broj poena timova?
- source_sentence: '''Super Jenki'' sistem uključuje pet događaja i 26 pojedinačnih
    opklada, takođe poznat kao kanadski sistem.'
  sentences:
  - Šta je 'Super Jenki' sistem klađenja?
  - Šta je procena verovatnoće?
  - Kako klađenje uživo funkcioniše u tenisu?
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-large
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.8260869565217391
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9565217391304348
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8260869565217391
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31884057971014484
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000007
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8260869565217391
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9565217391304348
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9271072095125116
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9021739130434783
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9021739130434783
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.8695652173913043
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8695652173913043
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333332
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000007
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8695652173913043
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9461678046583877
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9275362318840579
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9275362318840579
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.8260869565217391
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8260869565217391
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333332
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000007
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8260869565217391
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9301212722049728
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9057971014492753
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9057971014492753
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.782608695652174
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9565217391304348
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.782608695652174
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31884057971014484
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000007
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.782608695652174
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9565217391304348
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9091552965878422
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8782608695652173
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8782608695652173
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.8260869565217391
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9565217391304348
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9565217391304348
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8260869565217391
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31884057971014484
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19130434782608702
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000003
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8260869565217391
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9565217391304348
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9565217391304348
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9164054079968976
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8894927536231884
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8894927536231884
      name: Cosine Map@100
---

# SentenceTransformer based on intfloat/multilingual-e5-large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("luka023/proba")
# Run inference
sentences = [
    "'Super Jenki' sistem uključuje pet događaja i 26 pojedinačnih opklada, takođe poznat kao kanadski sistem.",
    "Šta je 'Super Jenki' sistem klađenja?",
    'Kako klađenje uživo funkcioniše u tenisu?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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.*
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## 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   | 0.8261     |
| cosine_accuracy@3   | 0.9565     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8261     |
| cosine_precision@3  | 0.3188     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8261     |
| cosine_recall@3     | 0.9565     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9271     |
| cosine_mrr@10       | 0.9022     |
| **cosine_map@100**  | **0.9022** |

#### 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   | 0.8696     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8696     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8696     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9462     |
| cosine_mrr@10       | 0.9275     |
| **cosine_map@100**  | **0.9275** |

#### 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   | 0.8261     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8261     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8261     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9301     |
| cosine_mrr@10       | 0.9058     |
| **cosine_map@100**  | **0.9058** |

#### 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   | 0.7826     |
| cosine_accuracy@3   | 0.9565     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.7826     |
| cosine_precision@3  | 0.3188     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.7826     |
| cosine_recall@3     | 0.9565     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9092     |
| cosine_mrr@10       | 0.8783     |
| **cosine_map@100**  | **0.8783** |

#### 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   | 0.8261     |
| cosine_accuracy@3   | 0.9565     |
| cosine_accuracy@5   | 0.9565     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8261     |
| cosine_precision@3  | 0.3188     |
| cosine_precision@5  | 0.1913     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8261     |
| cosine_recall@3     | 0.9565     |
| cosine_recall@5     | 0.9565     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9164     |
| cosine_mrr@10       | 0.8895     |
| **cosine_map@100**  | **0.8895** |

<!--
## 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

#### json

* Dataset: json
* Size: 198 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 198 samples:
  |         | positive                                                                           | anchor                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 19 tokens</li><li>mean: 33.76 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.87 tokens</li><li>max: 21 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                        | anchor                                                              |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
  | <code>Klađenje na ukupan broj poena timova podrazumeva predviđanje da li će jedan tim postići više ili manje poena od postavljene granice, nezavisno od konačnog ishoda.</code> | <code>Kako funkcioniše klađenje na ukupan broj poena timova?</code> |
  | <code>Konačan ishod podrazumeva klađenje na to ko će pobediti u utakmici, pri čemu postoje tri mogućnosti: pobeda domaćina, pobeda gosta ili nerešeno.</code>                   | <code>Šta znači klađenje na konačan ishod?</code>                   |
  | <code>Patent opklada uključuje tri događaja sa ukupno sedam pojedinačnih opklada: tri singl, tri dubl i jedna trostruka opklada.</code>                                         | <code>Šta je patent opklada?</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`: 32
- `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`: False
- `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`: 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`: False
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step  | 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 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 1.0     | 1     | 0.6717                 | 0.7663                 | 0.8229                 | 0.5755                | 0.8242                 |
| **2.0** | **2** | **0.7779**             | **0.8457**             | **0.8638**             | **0.7833**            | **0.8635**             |
| 3.0     | 4     | 0.8410                 | 0.8732                 | 0.8674                 | 0.8167                | 0.8659                 |
| 1.0     | 1     | 0.8410                 | 0.8732                 | 0.8674                 | 0.8167                | 0.8659                 |
| **2.0** | **2** | **0.8845**             | **0.8732**             | **0.9022**             | **0.858**             | **0.9022**             |
| 3.0     | 4     | 0.8783                 | 0.9058                 | 0.9275                 | 0.8895                | 0.9022                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 3.0.0
- 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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