---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3853
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: '"BY_RECEPTION_TIMESTAMP_DESTINATIONORDER_QOS" <

    "BY_SOURCE_TIMESTAMP_DESTINATIONORDER_QOS"'
  sentences:
  - What is the primary concept that the Discovery Server mechanism uses from the
    RTPS protocol?
  - What is the default state of the Verbosity Level component in the logging module?
  - What is the consequence of having a DataWriter kind that is lower than the DataReader
    kind in terms of DestinationOrderQosPolicy?
- source_sentence: '+-----------------------------------------+-------------------------+--------------------------------------------------------+

    | Data Member Name                        | Type                    | Default
    Value                                          |

    |=========================================|=========================|========================================================|

    | "kind"                                  | DurabilityQosPolicyKind | "VOLATILE_DURABILITY_QOS"
    for DataReaders              |

    |                                         |                         | "TRANSIENT_LOCAL_DURABILITY_QOS"
    for DataWriters       |

    +-----------------------------------------+-------------------------+--------------------------------------------------------+'
  sentences:
  - What is the default value of the "kind" data member for a DataReader in the DurabilityQoSPolicy?
  - What is the main concept of the SQL-like filter syntax used in ContentFilteredTopic
    API?
  - What is the purpose of the "<shared_dir>" value in the QoS configuration?
- source_sentence: "      git clone https://github.com/eProsima/Fast-DDS.git && cd\
    \ Fast-DDS\n      WORKSPACE=$PWD"
  sentences:
  - What is the primary function of the ThreadSettings parameter in the context of
    Fast DDS thread creation?
  - What is the primary requirement for installing eProsima Fast DDS library on QNX
    7.1 from sources?
  - What's the purpose of the "max_handshake_requests" property in the context of
    authentication handshake settings?
- source_sentence: 'This QoS Policy allows the configuration of the wire protocol.
    See

    "WireProtocolConfigQos".'
  sentences:
  - What is the primary purpose of the WireProtocolConfigQos policy in a DDS (Data
    Distribution Service) system?
  - What determines when a DataWriter sends consecutive liveliness messages, according
    to the LivelinessQosPolicy?
  - What is the purpose of the LivelinessQosPolicy in a DataReader's QoS settings?
- source_sentence: "* \"AUTOMATIC_LIVELINESS_QOS\": The service takes the responsibility\
    \ for\n  renewing the leases at the required rates, as long as the local\n  process\
    \ where the participant is running and the link connecting it\n  to remote participants\
    \ exists, the entities within the remote\n  participant will be considered alive.\
    \ This kind is suitable for\n  applications that only need to detect whether a\
    \ remote application\n  is still running."
  sentences:
  - What is the primary mechanism used by the service to ensure that a particular
    entity on the network remains considered "alive" when using the LivelinessQosPolicy
    with the "AUTOMATIC_ LIVELINESS_ QOS" kind?
  - What is the purpose of creating a "DomainParticipant" in the context of monitoring
    application development?
  - What is the purpose of loading an XML profiles file before creating entities in
    Fast DDS?
pipeline_tag: sentence-similarity
model-index:
- name: Fine tuning poc1-5e
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.3333333333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.49184149184149184
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5524475524475524
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6247086247086248
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3333333333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16394716394716394
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11048951048951047
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06247086247086246
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3333333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.49184149184149184
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5524475524475524
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6247086247086248
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4719611229721751
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4239057239057238
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.43117995796594344
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.331002331002331
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.48717948717948717
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5454545454545454
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.62004662004662
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.331002331002331
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16239316239316237
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10909090909090909
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.062004662004662
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.331002331002331
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.48717948717948717
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5454545454545454
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.62004662004662
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.46621244210597373
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4178830428830428
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.42502313070898473
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.31002331002331
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4731934731934732
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5431235431235432
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6083916083916084
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.31002331002331
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1577311577311577
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1086247086247086
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.060839160839160834
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.31002331002331
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4731934731934732
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5431235431235432
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6083916083916084
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4519785373832247
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4023217523217523
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4106739429542078
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.30303030303030304
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.46386946386946387
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5268065268065268
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5967365967365967
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.30303030303030304
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15462315462315462
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10536130536130535
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05967365967365966
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.30303030303030304
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.46386946386946387
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5268065268065268
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5967365967365967
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.44299689615589044
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.39438801938801926
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4031610579311292
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.27972027972027974
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4289044289044289
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.49417249417249415
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5641025641025641
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.27972027972027974
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.14296814296814295
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.09883449883449884
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05641025641025641
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.27972027972027974
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4289044289044289
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.49417249417249415
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5641025641025641
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.41745494156327173
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.37105672105672094
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3800930218379113
      name: Cosine Map@100
---

# Fine tuning poc1-5e

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("cferreiragonz/bge-base-fastdds-questions-5b-epochs")
# Run inference
sentences = [
    '* "AUTOMATIC_LIVELINESS_QOS": The service takes the responsibility for\n  renewing the leases at the required rates, as long as the local\n  process where the participant is running and the link connecting it\n  to remote participants exists, the entities within the remote\n  participant will be considered alive. This kind is suitable for\n  applications that only need to detect whether a remote application\n  is still running.',
    'What is the primary mechanism used by the service to ensure that a particular entity on the network remains considered "alive" when using the LivelinessQosPolicy with the "AUTOMATIC_ LIVELINESS_ QOS" kind?',
    'What is the purpose of loading an XML profiles file before creating entities in Fast DDS?',
]
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   | 0.3333     |
| cosine_accuracy@3   | 0.4918     |
| cosine_accuracy@5   | 0.5524     |
| cosine_accuracy@10  | 0.6247     |
| cosine_precision@1  | 0.3333     |
| cosine_precision@3  | 0.1639     |
| cosine_precision@5  | 0.1105     |
| cosine_precision@10 | 0.0625     |
| cosine_recall@1     | 0.3333     |
| cosine_recall@3     | 0.4918     |
| cosine_recall@5     | 0.5524     |
| cosine_recall@10    | 0.6247     |
| cosine_ndcg@10      | 0.472      |
| cosine_mrr@10       | 0.4239     |
| **cosine_map@100**  | **0.4312** |

#### 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   | 0.331     |
| cosine_accuracy@3   | 0.4872    |
| cosine_accuracy@5   | 0.5455    |
| cosine_accuracy@10  | 0.62      |
| cosine_precision@1  | 0.331     |
| cosine_precision@3  | 0.1624    |
| cosine_precision@5  | 0.1091    |
| cosine_precision@10 | 0.062     |
| cosine_recall@1     | 0.331     |
| cosine_recall@3     | 0.4872    |
| cosine_recall@5     | 0.5455    |
| cosine_recall@10    | 0.62      |
| cosine_ndcg@10      | 0.4662    |
| cosine_mrr@10       | 0.4179    |
| **cosine_map@100**  | **0.425** |

#### 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   | 0.31       |
| cosine_accuracy@3   | 0.4732     |
| cosine_accuracy@5   | 0.5431     |
| cosine_accuracy@10  | 0.6084     |
| cosine_precision@1  | 0.31       |
| cosine_precision@3  | 0.1577     |
| cosine_precision@5  | 0.1086     |
| cosine_precision@10 | 0.0608     |
| cosine_recall@1     | 0.31       |
| cosine_recall@3     | 0.4732     |
| cosine_recall@5     | 0.5431     |
| cosine_recall@10    | 0.6084     |
| cosine_ndcg@10      | 0.452      |
| cosine_mrr@10       | 0.4023     |
| **cosine_map@100**  | **0.4107** |

#### 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   | 0.303      |
| cosine_accuracy@3   | 0.4639     |
| cosine_accuracy@5   | 0.5268     |
| cosine_accuracy@10  | 0.5967     |
| cosine_precision@1  | 0.303      |
| cosine_precision@3  | 0.1546     |
| cosine_precision@5  | 0.1054     |
| cosine_precision@10 | 0.0597     |
| cosine_recall@1     | 0.303      |
| cosine_recall@3     | 0.4639     |
| cosine_recall@5     | 0.5268     |
| cosine_recall@10    | 0.5967     |
| cosine_ndcg@10      | 0.443      |
| cosine_mrr@10       | 0.3944     |
| **cosine_map@100**  | **0.4032** |

#### 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   | 0.2797     |
| cosine_accuracy@3   | 0.4289     |
| cosine_accuracy@5   | 0.4942     |
| cosine_accuracy@10  | 0.5641     |
| cosine_precision@1  | 0.2797     |
| cosine_precision@3  | 0.143      |
| cosine_precision@5  | 0.0988     |
| cosine_precision@10 | 0.0564     |
| cosine_recall@1     | 0.2797     |
| cosine_recall@3     | 0.4289     |
| cosine_recall@5     | 0.4942     |
| cosine_recall@10    | 0.5641     |
| cosine_ndcg@10      | 0.4175     |
| cosine_mrr@10       | 0.3711     |
| **cosine_map@100**  | **0.3801** |

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### 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`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: 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`: 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`: 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`: False
- `fp16`: True
- `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
- `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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6639     | 10     | 5.0927        | -                      | -                      | -                      | -                     | -                      |
| 0.9959     | 15     | -             | 0.3916                 | 0.3898                 | 0.4021                 | 0.3546                | 0.4027                 |
| 1.3278     | 20     | 3.3958        | -                      | -                      | -                      | -                     | -                      |
| 1.9917     | 30     | 2.6034        | 0.3893                 | 0.4034                 | 0.4163                 | 0.3719                | 0.4222                 |
| 2.6556     | 40     | 2.1012        | -                      | -                      | -                      | -                     | -                      |
| 2.9876     | 45     | -             | 0.3975                 | 0.4085                 | 0.4240                 | 0.3780                | 0.4291                 |
| 3.3195     | 50     | 1.8189        | -                      | -                      | -                      | -                     | -                      |
| **3.9834** | **60** | **1.715**     | **0.4029**             | **0.411**              | **0.4236**             | **0.3794**            | **0.4288**             |
| 4.6473     | 70     | 1.6089        | -                      | -                      | -                      | -                     | -                      |
| 4.9793     | 75     | -             | 0.4032                 | 0.4107                 | 0.4250                 | 0.3801                | 0.4312                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->