---
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: 'The environment variable accepts several types of units to specify
    the

    values of the parameters. Also, both lowercase and uppercase letters

    are valid. The following list shows the available units and their

    corresponding symbols:'
  sentences:
  - What is the primary purpose of using eProsima DDS Record and Replay in ROS 2,
    as described in the provided text?
  - What is the purpose of installing SWIG when setting up Fast DDS Python bindings?
  - What is the primary purpose of setting the "FASTDDS_ BUILTIN_TRANSPORTS" environment
    variable in Fast DDS?
- source_sentence: '+-----------------------+-----------------------------+-----------------------------+-----------------------------+

    | **StatusKind Value**  | **StatusKind Name**         | **Data field Name**         |
    **IDL Data field Type**     |

    +-----------------------+-----------------------------+-----------------------------+-----------------------------+

    | 7                     | "SampleLostInfo"            | sample_lost_status          |
    SampleLostStatus            |

    +-----------------------+-----------------------------+-----------------------------+-----------------------------+'
  sentences:
  - What is the purpose of the "SampleLostInfo" value in the Monitor Service Status
    Topic?
  - What is the primary method for installing the Fast DDS library and its dependencies
    on a Mac OS environment from sources?
  - What is a way for an application to request the creation of a DataReader with
    unique listening locators on Fast DDS?
- source_sentence: "   1. Create a DataReader for the previous type. Make sure that\
    \ the\n      DataReader does not have DataSharing disabled."
  sentences:
  - What is the purpose of the "SampleInfo" object returned when a sample is retrieved
    from the DataReader, in addition to the sample data?
  - What is the necessary condition to create a DataReader in Zero-Copy, as described
    in the provided steps?
  - What is the primary function of the "TransportConfigQos" QoS Policy, as described
    in the provided context?
- source_sentence: "Note:  Currently the \"generation_rank\" is not implemented, and\
    \ its value is\n  always set to \"0\". It will be implemented on a future release\
    \ of\n  *Fast DDS*."
  sentences:
  - What is the current state of the "generation_rank" feature in Fast DDS?
  - What is the purpose of the "disable_positive_ACKs" data member in the RTPSReliableReaderQos
    policy?
  - What is required to be implemented by a Custom Filter's factory, according to
    the provided interface?
- source_sentence: "  For further information about Fast DDS build system dependencies\n\
    \  regarding QNX 7.1, please refer to the Fast DDS Build system\n  dependencies\
    \ section."
  sentences:
  - What is required for installing eProsima Fast DDS on a QNX 7.1 target from sources?
  - What is the primary function of the "ordered_access" data member in the PresentationQosPolicy?
  - What is the primary purpose of the SubscriberListener class in terms of handling
    state changes on a Subscriber?
pipeline_tag: sentence-similarity
model-index:
- name: Fine tuning poc1
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.30536130536130535
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4988344988344988
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5547785547785548
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.627039627039627
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.30536130536130535
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16627816627816627
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11095571095571094
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0627039627039627
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.30536130536130535
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4988344988344988
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5547785547785548
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.627039627039627
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.46678781045558054
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.41558996558996547
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4221848409021621
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.29836829836829837
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.48484848484848486
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5431235431235432
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.627039627039627
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.29836829836829837
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1616161616161616
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1086247086247086
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0627039627039627
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.29836829836829837
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.48484848484848486
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5431235431235432
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.627039627039627
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4610322834562284
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.40834535834535834
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4148840457241853
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.3006993006993007
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4801864801864802
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5221445221445221
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6060606060606061
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3006993006993007
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16006216006216004
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10442890442890443
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0606060606060606
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3006993006993007
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4801864801864802
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5221445221445221
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6060606060606061
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.45215211249211645
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4032643282643281
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.41142292258910923
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.289044289044289
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.46153846153846156
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5174825174825175
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5944055944055944
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.289044289044289
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15384615384615385
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1034965034965035
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05944055944055943
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.289044289044289
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.46153846153846156
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5174825174825175
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5944055944055944
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4392655353082458
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.389960964960965
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.39812034059584056
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.2564102564102564
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4149184149184149
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.47785547785547783
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5547785547785548
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2564102564102564
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1383061383061383
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.09557109557109555
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05547785547785547
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2564102564102564
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4149184149184149
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.47785547785547783
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5547785547785548
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4019295040984946
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3534585784585784
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3621954461291384
      name: Cosine Map@100
---

# Fine tuning poc1

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")
# Run inference
sentences = [
    '  For further information about Fast DDS build system dependencies\n  regarding QNX 7.1, please refer to the Fast DDS Build system\n  dependencies section.',
    'What is required for installing eProsima Fast DDS on a QNX 7.1 target from sources?',
    'What is the primary purpose of the SubscriberListener class in terms of handling state changes on a Subscriber?',
]
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   | 0.3054     |
| cosine_accuracy@3   | 0.4988     |
| cosine_accuracy@5   | 0.5548     |
| cosine_accuracy@10  | 0.627      |
| cosine_precision@1  | 0.3054     |
| cosine_precision@3  | 0.1663     |
| cosine_precision@5  | 0.111      |
| cosine_precision@10 | 0.0627     |
| cosine_recall@1     | 0.3054     |
| cosine_recall@3     | 0.4988     |
| cosine_recall@5     | 0.5548     |
| cosine_recall@10    | 0.627      |
| cosine_ndcg@10      | 0.4668     |
| cosine_mrr@10       | 0.4156     |
| **cosine_map@100**  | **0.4222** |

#### 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   | 0.2984     |
| cosine_accuracy@3   | 0.4848     |
| cosine_accuracy@5   | 0.5431     |
| cosine_accuracy@10  | 0.627      |
| cosine_precision@1  | 0.2984     |
| cosine_precision@3  | 0.1616     |
| cosine_precision@5  | 0.1086     |
| cosine_precision@10 | 0.0627     |
| cosine_recall@1     | 0.2984     |
| cosine_recall@3     | 0.4848     |
| cosine_recall@5     | 0.5431     |
| cosine_recall@10    | 0.627      |
| cosine_ndcg@10      | 0.461      |
| cosine_mrr@10       | 0.4083     |
| **cosine_map@100**  | **0.4149** |

#### 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   | 0.3007     |
| cosine_accuracy@3   | 0.4802     |
| cosine_accuracy@5   | 0.5221     |
| cosine_accuracy@10  | 0.6061     |
| cosine_precision@1  | 0.3007     |
| cosine_precision@3  | 0.1601     |
| cosine_precision@5  | 0.1044     |
| cosine_precision@10 | 0.0606     |
| cosine_recall@1     | 0.3007     |
| cosine_recall@3     | 0.4802     |
| cosine_recall@5     | 0.5221     |
| cosine_recall@10    | 0.6061     |
| cosine_ndcg@10      | 0.4522     |
| cosine_mrr@10       | 0.4033     |
| **cosine_map@100**  | **0.4114** |

#### 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   | 0.289      |
| cosine_accuracy@3   | 0.4615     |
| cosine_accuracy@5   | 0.5175     |
| cosine_accuracy@10  | 0.5944     |
| cosine_precision@1  | 0.289      |
| cosine_precision@3  | 0.1538     |
| cosine_precision@5  | 0.1035     |
| cosine_precision@10 | 0.0594     |
| cosine_recall@1     | 0.289      |
| cosine_recall@3     | 0.4615     |
| cosine_recall@5     | 0.5175     |
| cosine_recall@10    | 0.5944     |
| cosine_ndcg@10      | 0.4393     |
| cosine_mrr@10       | 0.39       |
| **cosine_map@100**  | **0.3981** |

#### 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   | 0.2564     |
| cosine_accuracy@3   | 0.4149     |
| cosine_accuracy@5   | 0.4779     |
| cosine_accuracy@10  | 0.5548     |
| cosine_precision@1  | 0.2564     |
| cosine_precision@3  | 0.1383     |
| cosine_precision@5  | 0.0956     |
| cosine_precision@10 | 0.0555     |
| cosine_recall@1     | 0.2564     |
| cosine_recall@3     | 0.4149     |
| cosine_recall@5     | 0.4779     |
| cosine_recall@10    | 0.5548     |
| cosine_ndcg@10      | 0.4019     |
| cosine_mrr@10       | 0.3535     |
| **cosine_map@100**  | **0.3622** |

<!--
## 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`: 4
- `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`: 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`: 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     | 5.0047        | -                      | -                      | -                      | -                     | -                      |
| 0.9959     | 15     | -             | 0.3624                 | 0.3806                 | 0.3842                 | 0.3318                | 0.3864                 |
| 1.3278     | 20     | 3.3543        | -                      | -                      | -                      | -                     | -                      |
| 1.9917     | 30     | 2.5931        | 0.3886                 | 0.4016                 | 0.4103                 | 0.3603                | 0.4153                 |
| 2.6556     | 40     | 2.1763        | -                      | -                      | -                      | -                     | -                      |
| 2.9876     | 45     | -             | 0.3966                 | 0.4126                 | 0.4156                 | 0.3623                | 0.4205                 |
| 3.3195     | 50     | 2.0242        | -                      | -                      | -                      | -                     | -                      |
| **3.9834** | **60** | **1.9003**    | **0.3981**             | **0.4114**             | **0.4149**             | **0.3622**            | **0.4222**             |

* 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}
}
```

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