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bge-base-fastdds-summaries-20epochs-666seed

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:3877
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3877
- 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: <summary>The "_fastdds_statistics_sample_datas" topic tracks the
number of data messages or fragments sent by a DataWriter to deliver a single
sample, excluding built-in and statistics DataWriters.</summary>
sentences:
- " If several new data changes are received at once, the callbacks may\n be triggered\
\ just once, instead of once per change. The application\n must keep *reading*\
\ or *taking* until no new changes are available."
- 'The "_fastdds_statistics_sample_datas" statistics topic collects the
number of user''s data messages (or data fragments in case that the
message size is large enough to require RTPS fragmentation) that have
been sent by the user''s DataWriter to completely deliver a single
sample. This topic does not apply to builtin (related to Discovery)
and statistics DataWriters.'
- '+------------------------------------------------+-----------------------------------------+------------+-------------+
| Name | Description |
Values | Default |
|================================================|=========================================|============|=============|
| "<disable_heartbeat_piggyback>" | See DisableHeartbeatPiggyback. |
"bool" | "false" |
+------------------------------------------------+-----------------------------------------+------------+-------------+'
- source_sentence: 'The "enable_statistics_datawriter_with_profile()" method enables
a DataWriter by searching a specific XML profile, requiring two parameters: the
name of the XML profile and the name of the statistics topic to be enabled.'
sentences:
- '"enable_statistics_datawriter_with_profile()" method requires as
parameters:'
- "* **FIELDNAME**: is a reference to a field in the data-structure. The\n dot\
\ \".\" is used to navigate through nested structures. The number of\n dots that\
\ may be used in a FIELDNAME is unlimited. The FIELDNAME can\n refer to fields\
\ at any depth in the data structure. The names of the\n field are those specified\
\ in the IDL definition of the corresponding\n structure."
- " * The TopicQos describing the behavior of the Topic. If the\n provided\
\ value is \"TOPIC_QOS_DEFAULT\", the value of the Default\n TopicQos is used."
- source_sentence: '<summary> ParticipantResourceLimitsQos configures allocation limits
and physical memory usage for internal resources, including locators, participants,
readers, writers, send buffers, data limits, and content filter discovery information. '
sentences:
- "* \"max_properties\": Defines the maximum size, in octets, of the\n properties\
\ data in the local or remote participant."
- 'Log entries can be filtered upon consumption according to their
Category component using regular expressions. Each time an entry is
ready to be consumed, the category filter is applied using
"std::regex_search()". To set a category filter, member function
"Log::SetCategoryFilter()" is used:'
- '"create_datawriter_with_profile()" will return a null pointer if there
was an error during the operation, e.g. if the provided QoS is not
compatible or is not supported. It is advisable to check that the
returned value is a valid pointer.'
- source_sentence: <summary>The Fast DDS Statistics module enables data collection
and publication using DDS topics, which can be activated by setting "-DFASTDDS_STATISTICS=ON"
during CMake configuration.>
sentences:
- '
"set_default_subscriber_qos()" member function also accepts the
special value "SUBSCRIBER_QOS_DEFAULT" as input argument. This will
reset the current default SubscriberQos to default constructed value
"SubscriberQos()".'
- '+------------------------------------------------------------------------------+-------------------------------------------+
| Data Member Name |
Type |
|==============================================================================|===========================================|
| "last_instance_handle" |
"InstanceHandle_t" |
+------------------------------------------------------------------------------+-------------------------------------------+'
- "Note: Please refer to Statistics QoS Troubleshooting for any problems\n related\
\ to the statistics module.\n"
- source_sentence: The transport layer provides communication services between DDS
entities, using UDPv4, UDPv6, TCPv4, TCPv6, and SHM transports.
sentences:
- '* **TCPv4**: TCP communication over IPv4 (see TCP Transport).'
- 'The following table shows the supported primitive types and their
corresponding "TypeKind". The "TypeKind" is used to query the
DynamicTypeBuilderFactory for the specific primitive DynamicType.'
- " @annotation MyAnnotation\n {\n long value;\n string name;\n\
\ };"
pipeline_tag: sentence-similarity
model-index:
- name: BGE base Fast-DDS summaries
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.33410672853828305
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44547563805104406
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5034802784222738
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5661252900232019
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.33410672853828305
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14849187935034802
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10069605568445474
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05661252900232018
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.33410672853828305
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44547563805104406
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5034802784222738
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5661252900232019
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4437291164486755
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.40535023754281285
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4159956670067687
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.33642691415313225
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44779582366589327
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4965197215777262
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5777262180974478
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.33642691415313225
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14926527455529776
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09930394431554523
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.057772621809744774
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.33642691415313225
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44779582366589327
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4965197215777262
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5777262180974478
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.44632006141530195
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4056724855448751
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4154320968121733
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.3271461716937355
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44779582366589327
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4988399071925754
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5754060324825986
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3271461716937355
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14926527455529776
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09976798143851506
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05754060324825985
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3271461716937355
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44779582366589327
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4988399071925754
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5754060324825986
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.44144646221433803
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3997293116782675
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.41051122365814446
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.31554524361948955
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42923433874709976
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4802784222737819
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5754060324825986
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.31554524361948955
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1430781129156999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09605568445475636
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05754060324825985
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.31554524361948955
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42923433874709976
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4802784222737819
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5754060324825986
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4328383223462609
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.38895517990645573
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.39937008449735967
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.2853828306264501
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.41531322505800466
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46867749419953597
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5568445475638051
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2853828306264501
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13843774168600154
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09373549883990717
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0556844547563805
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2853828306264501
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.41531322505800466
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.46867749419953597
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5568445475638051
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4098284836140229
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.36409144477589944
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.37437465138771003
name: Cosine Map@100
---
# BGE base Fast-DDS summaries
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("juanlofer/bge-base-fastdds-summaries-20epochs-666seed")
# Run inference
sentences = [
'The transport layer provides communication services between DDS entities, using UDPv4, UDPv6, TCPv4, TCPv6, and SHM transports.',
'* **TCPv4**: TCP communication over IPv4 (see TCP Transport).',
'The following table shows the supported primitive types and their\ncorresponding "TypeKind". The "TypeKind" is used to query the\nDynamicTypeBuilderFactory for the specific primitive DynamicType.',
]
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 | 0.3341 |
| cosine_accuracy@3 | 0.4455 |
| cosine_accuracy@5 | 0.5035 |
| cosine_accuracy@10 | 0.5661 |
| cosine_precision@1 | 0.3341 |
| cosine_precision@3 | 0.1485 |
| cosine_precision@5 | 0.1007 |
| cosine_precision@10 | 0.0566 |
| cosine_recall@1 | 0.3341 |
| cosine_recall@3 | 0.4455 |
| cosine_recall@5 | 0.5035 |
| cosine_recall@10 | 0.5661 |
| cosine_ndcg@10 | 0.4437 |
| cosine_mrr@10 | 0.4054 |
| **cosine_map@100** | **0.416** |
#### 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 | 0.3364 |
| cosine_accuracy@3 | 0.4478 |
| cosine_accuracy@5 | 0.4965 |
| cosine_accuracy@10 | 0.5777 |
| cosine_precision@1 | 0.3364 |
| cosine_precision@3 | 0.1493 |
| cosine_precision@5 | 0.0993 |
| cosine_precision@10 | 0.0578 |
| cosine_recall@1 | 0.3364 |
| cosine_recall@3 | 0.4478 |
| cosine_recall@5 | 0.4965 |
| cosine_recall@10 | 0.5777 |
| cosine_ndcg@10 | 0.4463 |
| cosine_mrr@10 | 0.4057 |
| **cosine_map@100** | **0.4154** |
#### 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 | 0.3271 |
| cosine_accuracy@3 | 0.4478 |
| cosine_accuracy@5 | 0.4988 |
| cosine_accuracy@10 | 0.5754 |
| cosine_precision@1 | 0.3271 |
| cosine_precision@3 | 0.1493 |
| cosine_precision@5 | 0.0998 |
| cosine_precision@10 | 0.0575 |
| cosine_recall@1 | 0.3271 |
| cosine_recall@3 | 0.4478 |
| cosine_recall@5 | 0.4988 |
| cosine_recall@10 | 0.5754 |
| cosine_ndcg@10 | 0.4414 |
| cosine_mrr@10 | 0.3997 |
| **cosine_map@100** | **0.4105** |
#### 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 | 0.3155 |
| cosine_accuracy@3 | 0.4292 |
| cosine_accuracy@5 | 0.4803 |
| cosine_accuracy@10 | 0.5754 |
| cosine_precision@1 | 0.3155 |
| cosine_precision@3 | 0.1431 |
| cosine_precision@5 | 0.0961 |
| cosine_precision@10 | 0.0575 |
| cosine_recall@1 | 0.3155 |
| cosine_recall@3 | 0.4292 |
| cosine_recall@5 | 0.4803 |
| cosine_recall@10 | 0.5754 |
| cosine_ndcg@10 | 0.4328 |
| cosine_mrr@10 | 0.389 |
| **cosine_map@100** | **0.3994** |
#### 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 | 0.2854 |
| cosine_accuracy@3 | 0.4153 |
| cosine_accuracy@5 | 0.4687 |
| cosine_accuracy@10 | 0.5568 |
| cosine_precision@1 | 0.2854 |
| cosine_precision@3 | 0.1384 |
| cosine_precision@5 | 0.0937 |
| cosine_precision@10 | 0.0557 |
| cosine_recall@1 | 0.2854 |
| cosine_recall@3 | 0.4153 |
| cosine_recall@5 | 0.4687 |
| cosine_recall@10 | 0.5568 |
| cosine_ndcg@10 | 0.4098 |
| cosine_mrr@10 | 0.3641 |
| **cosine_map@100** | **0.3744** |
<!--
## 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`: 20
- `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`: 20
- `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 |
|:-----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6584 | 10 | 5.9441 | - | - | - | - | - |
| 0.9877 | 15 | - | 0.3686 | 0.3792 | 0.3819 | 0.3414 | 0.3795 |
| 1.3128 | 20 | 4.7953 | - | - | - | - | - |
| 1.9712 | 30 | 3.77 | 0.3854 | 0.3963 | 0.3962 | 0.3682 | 0.3995 |
| 2.6255 | 40 | 2.9211 | - | - | - | - | - |
| 2.9547 | 45 | - | 0.3866 | 0.3919 | 0.3958 | 0.3759 | 0.3963 |
| 3.2798 | 50 | 2.4548 | - | - | - | - | - |
| 3.9383 | 60 | 2.0513 | - | - | - | - | - |
| 4.0041 | 61 | - | 0.3808 | 0.4018 | 0.3980 | 0.3647 | 0.3962 |
| 4.5926 | 70 | 1.5898 | - | - | - | - | - |
| 4.9877 | 76 | - | 0.3829 | 0.4029 | 0.4035 | 0.3625 | 0.4014 |
| 5.2469 | 80 | 1.4677 | - | - | - | - | - |
| 5.9053 | 90 | 1.1974 | - | - | - | - | - |
| 5.9712 | 91 | - | 0.3918 | 0.4006 | 0.4041 | 0.3654 | 0.4033 |
| 6.5597 | 100 | 0.9285 | - | - | - | - | - |
| 6.9547 | 106 | - | 0.3914 | 0.4019 | 0.4033 | 0.3678 | 0.4014 |
| 7.2140 | 110 | 0.9214 | - | - | - | - | - |
| 7.8724 | 120 | 0.8141 | - | - | - | - | - |
| 8.0041 | 122 | - | 0.3914 | 0.3993 | 0.4071 | 0.3670 | 0.4027 |
| 8.5267 | 130 | 0.6706 | - | - | - | - | - |
| 8.9877 | 137 | - | 0.3903 | 0.4033 | 0.4060 | 0.3721 | 0.4060 |
| 9.1811 | 140 | 0.6388 | - | - | - | - | - |
| 9.8395 | 150 | 0.5466 | - | - | - | - | - |
| 9.9712 | 152 | - | 0.3915 | 0.4020 | 0.4079 | 0.3673 | 0.4046 |
| 10.4938 | 160 | 0.466 | - | - | - | - | - |
| 10.9547 | 167 | - | 0.3963 | 0.4069 | 0.4112 | 0.3697 | 0.4078 |
| 11.1481 | 170 | 0.4709 | - | - | - | - | - |
| 11.8066 | 180 | 0.437 | - | - | - | - | - |
| 12.0041 | 183 | - | 0.4003 | 0.4051 | 0.4096 | 0.3701 | 0.4059 |
| 12.4609 | 190 | 0.3678 | - | - | - | - | - |
| 12.9877 | 198 | - | 0.3976 | 0.4075 | 0.4088 | 0.3713 | 0.4080 |
| 13.1152 | 200 | 0.3944 | - | - | - | - | - |
| 13.7737 | 210 | 0.361 | - | - | - | - | - |
| 13.9712 | 213 | - | 0.3966 | 0.4091 | 0.4096 | 0.3724 | 0.4107 |
| 14.4280 | 220 | 0.2977 | - | - | - | - | - |
| 14.9547 | 228 | - | 0.3979 | 0.4102 | 0.4149 | 0.3744 | 0.4143 |
| 15.0823 | 230 | 0.3306 | - | - | - | - | - |
| 15.7407 | 240 | 0.3075 | - | - | - | - | - |
| **16.0041** | **244** | **-** | **0.3991** | **0.4102** | **0.4156** | **0.3726** | **0.4148** |
| 16.3951 | 250 | 0.2777 | - | - | - | - | - |
| 16.9877 | 259 | - | 0.3990 | 0.4101 | 0.4154 | 0.3743 | 0.4167 |
| 17.0494 | 260 | 0.3044 | - | - | - | - | - |
| 17.7078 | 270 | 0.2885 | - | - | - | - | - |
| 17.9712 | 274 | - | 0.3991 | 0.4099 | 0.4153 | 0.3746 | 0.4167 |
| 18.3621 | 280 | 0.2862 | - | - | - | - | - |
| 18.9547 | 289 | - | 0.3994 | 0.4105 | 0.4154 | 0.3743 | 0.4156 |
| 19.0165 | 290 | 0.2974 | - | - | - | - | - |
| 19.6749 | 300 | 0.2648 | 0.3994 | 0.4105 | 0.4154 | 0.3744 | 0.4160 |
* 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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