---
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: '

    UDPv6TransportDescriptor

    ------------------------'
  sentences:
  - What is the primary purpose of the "Status" objects in the context of entities?
  - What is the main concept that this piece of code demonstrates, and how do the
    provided topology and QoS policy settings relate to it?
  - What is the primary characteristic of UDP transport in terms of connection establishment?
- source_sentence: 'With a fragment size of 64 kB, the Publisher has to send about
    1100

    fragments to send the whole file. A possible configuration for this

    scenario could be:'
  sentences:
  - What is the likely reason why the Publisher needs to use "RELIABLE_ RELIABILITY_QOS"
    in this scenario?
  - What is the effect of defining a custom Metatraffic Unicast Locators on the behavior
    of a DomainParticipant?
  - What is the primary function of the transport layer in DDS, as described in the
    provided context?
- source_sentence: '+------------------------------------+---------------------------------------------------+------------+

    | QosPolicy class                    | Accessor/Mutator                                  |
    Mutable    |

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

    | RTPSEndpointQos                    | "endpoint()"                                      |
    No         |

    +------------------------------------+---------------------------------------------------+------------+'
  sentences:
  - What is the effect of setting "ON" as the DataSharingKind in the context of data-sharing
    delivery?
  - What is the purpose of the RTPSEndpointQos class in the context of DataWriter
    QoS policies?
  - What is the primary purpose of the RTPSEndpointQos policy in a DDS (Data Distribution
    Service) system?
- source_sentence: "Note:  When \"non_blocking_send\" is set to \"true\", send operations\
    \ will\n  return immediately if the send buffer might get full, but no error\n\
    \  will be returned to the upper layer. This means that the application\n  will\
    \ behave as if the packet is sent and lost.When set to \"false\",\n  send operations\
    \ will block until the network buffer has space for\n  the packet."
  sentences:
  - What happens when "non_blocking_send" is set to "true" in TCP transport?
  - What is the purpose of the "<default_external_unicast_locators>" element in the
    RTPS configuration?
  - What is the purpose of the "<enabled>" value in the DisablePositiveAcks QoS policy?
- source_sentence: 'After calling the "DataReader::read()" or "DataReader::take()"

    operations, accessing the data on the returned sequences is quite

    easy. The sequences API provides a **length()** operation returning

    the number of elements in the collections. The application code just

    needs to check this value and use the **[]** operator to access the

    corresponding elements. Elements on the DDS data sequence should only

    be accessed when the corresponding element on the SampleInfo sequence

    indicate that valid data is present. When using Data Sharing, it is

    also important to check that the sample is valid (i.e, not replaced,

    refer to DataReader and DataWriter history coupling for further

    information in this regard).'
  sentences:
  - What is the primary method described in the text for accessing data on returned
    sequences after calling "DataReader::read()" or "DataReader::take()" operations?
  - What is the primary advantage of using Shared Memory Transport (SHM) compared
    to other network transports like UDP/TCP?
  - What are the steps to install Fast DDS library, Python bindings, and Gen generation
    tool from sources in a Linux environment?
pipeline_tag: sentence-similarity
model-index:
- name: Fine tuning poc1-30e
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.34265734265734266
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5291375291375291
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5757575757575758
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6643356643356644
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.34265734265734266
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.17637917637917636
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11515151515151513
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06643356643356643
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.34265734265734266
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5291375291375291
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5757575757575758
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6643356643356644
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4999219586168879
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.44783734783734785
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.45732757969458965
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.3333333333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5314685314685315
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5804195804195804
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.655011655011655
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3333333333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.17715617715617715
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11608391608391608
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0655011655011655
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3333333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5314685314685315
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5804195804195804
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.655011655011655
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4931410715247713
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.44150664150664165
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4520914166409126
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.331002331002331
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5384615384615384
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5734265734265734
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.662004662004662
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.331002331002331
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1794871794871795
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11468531468531468
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0662004662004662
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.331002331002331
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5384615384615384
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5734265734265734
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.662004662004662
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4946456648216315
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4414687164687165
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4517532849343265
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.32867132867132864
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5291375291375291
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.578088578088578
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6643356643356644
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.32867132867132864
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.17637917637917636
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11561771561771561
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06643356643356643
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.32867132867132864
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5291375291375291
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.578088578088578
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6643356643356644
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.491729303526411
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4370564620564619
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4465064100234966
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.317016317016317
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5058275058275058
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5734265734265734
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.655011655011655
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.317016317016317
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1686091686091686
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.11468531468531468
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06550116550116548
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.317016317016317
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5058275058275058
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5734265734265734
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.655011655011655
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4805357725353263
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.42515355015355016
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.43416870212536746
      name: Cosine Map@100
---

# Fine tuning poc1-30e

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-30-epochs")
# Run inference
sentences = [
    'After calling the "DataReader::read()" or "DataReader::take()"\noperations, accessing the data on the returned sequences is quite\neasy. The sequences API provides a **length()** operation returning\nthe number of elements in the collections. The application code just\nneeds to check this value and use the **[]** operator to access the\ncorresponding elements. Elements on the DDS data sequence should only\nbe accessed when the corresponding element on the SampleInfo sequence\nindicate that valid data is present. When using Data Sharing, it is\nalso important to check that the sample is valid (i.e, not replaced,\nrefer to DataReader and DataWriter history coupling for further\ninformation in this regard).',
    'What is the primary method described in the text for accessing data on returned sequences after calling "DataReader::read()" or "DataReader::take()" operations?',
    'What are the steps to install Fast DDS library, Python bindings, and Gen generation tool from sources in a Linux environment?',
]
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   | 0.3427     |
| cosine_accuracy@3   | 0.5291     |
| cosine_accuracy@5   | 0.5758     |
| cosine_accuracy@10  | 0.6643     |
| cosine_precision@1  | 0.3427     |
| cosine_precision@3  | 0.1764     |
| cosine_precision@5  | 0.1152     |
| cosine_precision@10 | 0.0664     |
| cosine_recall@1     | 0.3427     |
| cosine_recall@3     | 0.5291     |
| cosine_recall@5     | 0.5758     |
| cosine_recall@10    | 0.6643     |
| cosine_ndcg@10      | 0.4999     |
| cosine_mrr@10       | 0.4478     |
| **cosine_map@100**  | **0.4573** |

#### 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   | 0.3333     |
| cosine_accuracy@3   | 0.5315     |
| cosine_accuracy@5   | 0.5804     |
| cosine_accuracy@10  | 0.655      |
| cosine_precision@1  | 0.3333     |
| cosine_precision@3  | 0.1772     |
| cosine_precision@5  | 0.1161     |
| cosine_precision@10 | 0.0655     |
| cosine_recall@1     | 0.3333     |
| cosine_recall@3     | 0.5315     |
| cosine_recall@5     | 0.5804     |
| cosine_recall@10    | 0.655      |
| cosine_ndcg@10      | 0.4931     |
| cosine_mrr@10       | 0.4415     |
| **cosine_map@100**  | **0.4521** |

#### 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   | 0.331      |
| cosine_accuracy@3   | 0.5385     |
| cosine_accuracy@5   | 0.5734     |
| cosine_accuracy@10  | 0.662      |
| cosine_precision@1  | 0.331      |
| cosine_precision@3  | 0.1795     |
| cosine_precision@5  | 0.1147     |
| cosine_precision@10 | 0.0662     |
| cosine_recall@1     | 0.331      |
| cosine_recall@3     | 0.5385     |
| cosine_recall@5     | 0.5734     |
| cosine_recall@10    | 0.662      |
| cosine_ndcg@10      | 0.4946     |
| cosine_mrr@10       | 0.4415     |
| **cosine_map@100**  | **0.4518** |

#### 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   | 0.3287     |
| cosine_accuracy@3   | 0.5291     |
| cosine_accuracy@5   | 0.5781     |
| cosine_accuracy@10  | 0.6643     |
| cosine_precision@1  | 0.3287     |
| cosine_precision@3  | 0.1764     |
| cosine_precision@5  | 0.1156     |
| cosine_precision@10 | 0.0664     |
| cosine_recall@1     | 0.3287     |
| cosine_recall@3     | 0.5291     |
| cosine_recall@5     | 0.5781     |
| cosine_recall@10    | 0.6643     |
| cosine_ndcg@10      | 0.4917     |
| cosine_mrr@10       | 0.4371     |
| **cosine_map@100**  | **0.4465** |

#### 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   | 0.317      |
| cosine_accuracy@3   | 0.5058     |
| cosine_accuracy@5   | 0.5734     |
| cosine_accuracy@10  | 0.655      |
| cosine_precision@1  | 0.317      |
| cosine_precision@3  | 0.1686     |
| cosine_precision@5  | 0.1147     |
| cosine_precision@10 | 0.0655     |
| cosine_recall@1     | 0.317      |
| cosine_recall@3     | 0.5058     |
| cosine_recall@5     | 0.5734     |
| cosine_recall@10    | 0.655      |
| cosine_ndcg@10      | 0.4805     |
| cosine_mrr@10       | 0.4252     |
| **cosine_map@100**  | **0.4342** |

<!--
## 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 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`: 30
- `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`: 30
- `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      | 5.6138        | -                      | -                      | -                      | -                     | -                      |
| 0.9959      | 15      | -             | 0.3594                 | 0.3735                 | 0.3723                 | 0.3161                | 0.3807                 |
| 1.3278      | 20      | 4.9173        | -                      | -                      | -                      | -                     | -                      |
| 1.9917      | 30      | 3.7581        | 0.3874                 | 0.4014                 | 0.4026                 | 0.3729                | 0.4032                 |
| 2.6556      | 40      | 3.0018        | -                      | -                      | -                      | -                     | -                      |
| 2.9876      | 45      | -             | 0.4031                 | 0.4200                 | 0.4212                 | 0.3858                | 0.4223                 |
| 3.3195      | 50      | 2.5035        | -                      | -                      | -                      | -                     | -                      |
| 3.9834      | 60      | 1.9031        | 0.4187                 | 0.4303                 | 0.4178                 | 0.3958                | 0.4291                 |
| 4.6473      | 70      | 1.474         | -                      | -                      | -                      | -                     | -                      |
| 4.9793      | 75      | -             | 0.4293                 | 0.4332                 | 0.4318                 | 0.4172                | 0.4401                 |
| 5.3112      | 80      | 1.2801        | -                      | -                      | -                      | -                     | -                      |
| 5.9751      | 90      | 0.9577        | 0.4397                 | 0.4382                 | 0.4444                 | 0.4275                | 0.4518                 |
| 6.6390      | 100     | 0.7539        | -                      | -                      | -                      | -                     | -                      |
| 6.9710      | 105     | -             | 0.4434                 | 0.4414                 | 0.4496                 | 0.4262                | 0.4466                 |
| 7.3029      | 110     | 0.694         | -                      | -                      | -                      | -                     | -                      |
| 7.9668      | 120     | 0.5147        | 0.4423                 | 0.4488                 | 0.4507                 | 0.4358                | 0.4495                 |
| 8.6307      | 130     | 0.4589        | -                      | -                      | -                      | -                     | -                      |
| 8.9627      | 135     | -             | 0.4488                 | 0.4575                 | 0.4544                 | 0.4407                | 0.4493                 |
| 9.2946      | 140     | 0.3843        | -                      | -                      | -                      | -                     | -                      |
| 9.9585      | 150     | 0.3506        | 0.4521                 | 0.4465                 | 0.4559                 | 0.4420                | 0.4485                 |
| 10.6224     | 160     | 0.2723        | -                      | -                      | -                      | -                     | -                      |
| 10.9544     | 165     | -             | 0.4497                 | 0.4435                 | 0.4499                 | 0.4304                | 0.4453                 |
| 11.2863     | 170     | 0.2555        | -                      | -                      | -                      | -                     | -                      |
| 11.9502     | 180     | 0.2077        | 0.4448                 | 0.4472                 | 0.4468                 | 0.4287                | 0.4453                 |
| 12.6141     | 190     | 0.1894        | -                      | -                      | -                      | -                     | -                      |
| **12.9461** | **195** | **-**         | **0.4516**             | **0.4463**             | **0.4566**             | **0.4336**            | **0.452**              |
| 13.2780     | 200     | 0.1725        | -                      | -                      | -                      | -                     | -                      |
| 13.9419     | 210     | 0.1395        | 0.4528                 | 0.4520                 | 0.4561                 | 0.4333                | 0.4534                 |
| 14.6058     | 220     | 0.155         | -                      | -                      | -                      | -                     | -                      |
| 14.9378     | 225     | -             | 0.4461                 | 0.4491                 | 0.4527                 | 0.4369                | 0.4517                 |
| 15.2697     | 230     | 0.132         | -                      | -                      | -                      | -                     | -                      |
| 15.9336     | 240     | 0.1148        | -                      | -                      | -                      | -                     | -                      |
| 16.0        | 241     | -             | 0.4482                 | 0.4537                 | 0.4540                 | 0.4303                | 0.4538                 |
| 16.5975     | 250     | 0.1061        | -                      | -                      | -                      | -                     | -                      |
| 16.9959     | 256     | -             | 0.4464                 | 0.4538                 | 0.4551                 | 0.4294                | 0.4577                 |
| 17.2614     | 260     | 0.0961        | -                      | -                      | -                      | -                     | -                      |
| 17.9253     | 270     | 0.087         | -                      | -                      | -                      | -                     | -                      |
| 17.9917     | 271     | -             | 0.4485                 | 0.4483                 | 0.4495                 | 0.4326                | 0.4568                 |
| 18.5892     | 280     | 0.1009        | -                      | -                      | -                      | -                     | -                      |
| 18.9876     | 286     | -             | 0.4483                 | 0.4517                 | 0.4545                 | 0.4396                | 0.4565                 |
| 19.2531     | 290     | 0.0854        | -                      | -                      | -                      | -                     | -                      |
| 19.9170     | 300     | 0.073         | -                      | -                      | -                      | -                     | -                      |
| 19.9834     | 301     | -             | 0.4473                 | 0.4502                 | 0.4521                 | 0.4349                | 0.4548                 |
| 20.5809     | 310     | 0.0726        | -                      | -                      | -                      | -                     | -                      |
| 20.9793     | 316     | -             | 0.4466                 | 0.4525                 | 0.4538                 | 0.4341                | 0.4583                 |
| 21.2448     | 320     | 0.0747        | -                      | -                      | -                      | -                     | -                      |
| 21.9087     | 330     | 0.0621        | -                      | -                      | -                      | -                     | -                      |
| 21.9751     | 331     | -             | 0.4441                 | 0.4537                 | 0.4534                 | 0.4388                | 0.4564                 |
| 22.5726     | 340     | 0.0682        | -                      | -                      | -                      | -                     | -                      |
| 22.9710     | 346     | -             | 0.4454                 | 0.4529                 | 0.4544                 | 0.4385                | 0.4589                 |
| 23.2365     | 350     | 0.0612        | -                      | -                      | -                      | -                     | -                      |
| 23.9004     | 360     | 0.0546        | -                      | -                      | -                      | -                     | -                      |
| 23.9668     | 361     | -             | 0.4464                 | 0.4494                 | 0.4551                 | 0.4381                | 0.4567                 |
| 24.5643     | 370     | 0.0599        | -                      | -                      | -                      | -                     | -                      |
| 24.9627     | 376     | -             | 0.4465                 | 0.4506                 | 0.4553                 | 0.4363                | 0.4567                 |
| 25.2282     | 380     | 0.0591        | -                      | -                      | -                      | -                     | -                      |
| 25.8921     | 390     | 0.0562        | -                      | -                      | -                      | -                     | -                      |
| 25.9585     | 391     | -             | 0.4454                 | 0.4515                 | 0.4532                 | 0.4343                | 0.4575                 |
| 26.5560     | 400     | 0.0623        | -                      | -                      | -                      | -                     | -                      |
| 26.9544     | 406     | -             | 0.4452                 | 0.4531                 | 0.4544                 | 0.4342                | 0.4573                 |
| 27.2199     | 410     | 0.061         | -                      | -                      | -                      | -                     | -                      |
| 27.8838     | 420     | 0.053         | -                      | -                      | -                      | -                     | -                      |
| 27.9502     | 421     | -             | 0.4454                 | 0.4514                 | 0.4533                 | 0.4330                | 0.4573                 |
| 28.5477     | 430     | 0.0564        | -                      | -                      | -                      | -                     | -                      |
| 28.9461     | 436     | -             | 0.4465                 | 0.4516                 | 0.4533                 | 0.4338                | 0.4562                 |
| 29.2116     | 440     | 0.056         | -                      | -                      | -                      | -                     | -                      |
| 29.8755     | 450     | 0.0586        | 0.4465                 | 0.4518                 | 0.4521                 | 0.4342                | 0.4573                 |

* 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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