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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:19992
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Role: Full Stack Engineer
    Skills: JavaScript, APIs, React, Databases, Node.js'
  sentences:
  - 'Job Title: Product Manager
    Skills: Roadmap, Stakeholders, PRD, Agile, Strategy'
  - 'Job Title: Full Stack Engineer
    Skills: JavaScript, APIs, React, Databases, Node.js'
  - 'Job Title: Full Stack Engineer
    Skills: JavaScript, APIs, React, Databases, Node.js'
- source_sentence: 'Role: Mechanical Engineer
    Skills: CAD, Thermodynamics, Manufacturing'
  sentences:
  - 'Job Title: Frontend Engineer
    Skills: JavaScript, React, CSS, HTML, UI'
  - 'Job Title: Operations Manager
    Skills: KPIs, Process Improvement, Planning, Stakeholders'
  - 'Job Title: Mechanical Engineer
    Skills: CAD, Thermodynamics, Manufacturing'
- source_sentence: 'Role: Backend Engineer
    Skills: Python, APIs, Databases, Docker, OOP'
  sentences:
  - 'Job Title: Full Stack Engineer
    Skills: JavaScript, APIs, React, Databases, Node.js'
  - 'Job Title: Backend Engineer
    Skills: Python, APIs, Databases, Docker, OOP'
  - 'Job Title: Backend Engineer
    Skills: Python, APIs, Databases, Docker, OOP'
- source_sentence: 'Role: Civil Engineer
    Skills: AutoCAD, Structural Analysis, Construction, Planning'
  sentences:
  - 'Job Title: Mechanical Engineer
    Skills: CAD, Thermodynamics, Manufacturing'
  - 'Job Title: Mechanical Engineer
    Skills: CAD, Thermodynamics, Manufacturing'
  - 'Job Title: Civil Engineer
    Skills: AutoCAD, Structural Analysis, Construction, Planning'
- source_sentence: 'Role: Data Scientist
    Skills: Python, Statistics, Pandas, ML, Visualization, Modeling'
  sentences:
  - 'Job Title: Mechanical Engineer
    Skills: CAD, Thermodynamics, Manufacturing'
  - 'Job Title: Data Scientist
    Skills: Python, Statistics, Pandas, ML, Visualization, Modeling'
  - 'Job Title: Civil Engineer
    Skills: AutoCAD, Structural Analysis, Construction, Planning'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Role: Data Scientist\nSkills: Python, Statistics, Pandas, ML, Visualization, Modeling',
    'Job Title: Data Scientist\nSkills: Python, Statistics, Pandas, ML, Visualization, Modeling',
    'Job Title: Civil Engineer\nSkills: AutoCAD, Structural Analysis, Construction, Planning',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9988, 0.2972],
#         [0.9988, 1.0000, 0.3066],
#         [0.2972, 0.3066, 1.0000]])
```

<!--
### 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.*
-->

<!--
## 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 Dataset

#### Unnamed Dataset

* Size: 19,992 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 16 tokens</li><li>mean: 19.8 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 20.8 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                         | sentence_1                                                                                              |
  |:---------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
  | <code>Role: Operations Manager<br>Skills: KPIs, Process Improvement, Planning, Stakeholders</code> | <code>Job Title: Operations Manager<br>Skills: KPIs, Process Improvement, Planning, Stakeholders</code> |
  | <code>Role: Frontend Engineer<br>Skills: JavaScript, React, CSS, HTML, UI</code>                   | <code>Job Title: Frontend Engineer<br>Skills: JavaScript, React, CSS, HTML, UI</code>                   |
  | <code>Role: Full Stack Engineer<br>Skills: JavaScript, APIs, React, Databases, Node.js</code>      | <code>Job Title: Full Stack Engineer<br>Skills: JavaScript, APIs, React, Databases, Node.js</code>      |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 96
- `per_device_eval_batch_size`: 96
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 96
- `num_train_epochs`: 3
- `max_steps`: -1
- `learning_rate`: 5e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1
- `label_smoothing_factor`: 0.0
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: trackio
- `eval_strategy`: no
- `per_device_eval_batch_size`: 96
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 2.3923 | 500  | 2.2303        |


### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 5.3.0
- Transformers: 5.3.0
- PyTorch: 2.10.0+cu130
- Accelerate: 1.13.0
- Datasets: 4.7.0
- Tokenizers: 0.22.2

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}
```

<!--
## 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.*
-->

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:19992
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: 'Role: Full Stack Engineer

	Skills: JavaScript, APIs, React, Databases, Node.js'
	sentences:
	- 'Job Title: Product Manager

	Skills: Roadmap, Stakeholders, PRD, Agile, Strategy'
	- 'Job Title: Full Stack Engineer

	Skills: JavaScript, APIs, React, Databases, Node.js'
	- 'Job Title: Full Stack Engineer

	Skills: JavaScript, APIs, React, Databases, Node.js'
	- source_sentence: 'Role: Mechanical Engineer

	Skills: CAD, Thermodynamics, Manufacturing'
	sentences:
	- 'Job Title: Frontend Engineer

	Skills: JavaScript, React, CSS, HTML, UI'
	- 'Job Title: Operations Manager

	Skills: KPIs, Process Improvement, Planning, Stakeholders'
	- 'Job Title: Mechanical Engineer

	Skills: CAD, Thermodynamics, Manufacturing'
	- source_sentence: 'Role: Backend Engineer

	Skills: Python, APIs, Databases, Docker, OOP'
	sentences:
	- 'Job Title: Full Stack Engineer

	Skills: JavaScript, APIs, React, Databases, Node.js'
	- 'Job Title: Backend Engineer

	Skills: Python, APIs, Databases, Docker, OOP'
	- 'Job Title: Backend Engineer

	Skills: Python, APIs, Databases, Docker, OOP'
	- source_sentence: 'Role: Civil Engineer

	Skills: AutoCAD, Structural Analysis, Construction, Planning'
	sentences:
	- 'Job Title: Mechanical Engineer

	Skills: CAD, Thermodynamics, Manufacturing'
	- 'Job Title: Mechanical Engineer

	Skills: CAD, Thermodynamics, Manufacturing'
	- 'Job Title: Civil Engineer

	Skills: AutoCAD, Structural Analysis, Construction, Planning'
	- source_sentence: 'Role: Data Scientist

	Skills: Python, Statistics, Pandas, ML, Visualization, Modeling'
	sentences:
	- 'Job Title: Mechanical Engineer

	Skills: CAD, Thermodynamics, Manufacturing'
	- 'Job Title: Data Scientist

	Skills: Python, Statistics, Pandas, ML, Visualization, Modeling'
	- 'Job Title: Civil Engineer

	Skills: AutoCAD, Structural Analysis, Construction, Planning'
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer

	This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-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: [Unknown](https://huggingface.co/unknown) -->
	- Maximum Sequence Length: 256 tokens
	- Output Dimensionality: 384 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
	(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'Role: Data Scientist\nSkills: Python, Statistics, Pandas, ML, Visualization, Modeling',
	'Job Title: Data Scientist\nSkills: Python, Statistics, Pandas, ML, Visualization, Modeling',
	'Job Title: Civil Engineer\nSkills: AutoCAD, Structural Analysis, Construction, Planning',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities)
	# tensor([[1.0000, 0.9988, 0.2972],
	# [0.9988, 1.0000, 0.3066],
	# [0.2972, 0.3066, 1.0000]])
	```

	<!--
	### 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.
	-->

	<!--
	## 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 Dataset

	#### Unnamed Dataset

	* Size: 19,992 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 16 tokens</li><li>mean: 19.8 tokens</li><li>max: 27 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 20.8 tokens</li><li>max: 28 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:---------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------\|
	\| <code>Role: Operations Manager<br>Skills: KPIs, Process Improvement, Planning, Stakeholders</code> \| <code>Job Title: Operations Manager<br>Skills: KPIs, Process Improvement, Planning, Stakeholders</code> \|
	\| <code>Role: Frontend Engineer<br>Skills: JavaScript, React, CSS, HTML, UI</code> \| <code>Job Title: Frontend Engineer<br>Skills: JavaScript, React, CSS, HTML, UI</code> \|
	\| <code>Role: Full Stack Engineer<br>Skills: JavaScript, APIs, React, Databases, Node.js</code> \| <code>Job Title: Full Stack Engineer<br>Skills: JavaScript, APIs, React, Databases, Node.js</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim",
	"gather_across_devices": false,
	"directions": [
	"query_to_doc"
	],
	"partition_mode": "joint",
	"hardness_mode": null,
	"hardness_strength": 0.0
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 96
	- `per_device_eval_batch_size`: 96
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `per_device_train_batch_size`: 96
	- `num_train_epochs`: 3
	- `max_steps`: -1
	- `learning_rate`: 5e-05
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: None
	- `warmup_steps`: 0
	- `optim`: adamw_torch_fused
	- `optim_args`: None
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `optim_target_modules`: None
	- `gradient_accumulation_steps`: 1
	- `average_tokens_across_devices`: True
	- `max_grad_norm`: 1
	- `label_smoothing_factor`: 0.0
	- `bf16`: False
	- `fp16`: False
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `use_cache`: False
	- `neftune_noise_alpha`: None
	- `torch_empty_cache_steps`: None
	- `auto_find_batch_size`: False
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `include_num_input_tokens_seen`: no
	- `log_level`: passive
	- `log_level_replica`: warning
	- `disable_tqdm`: False
	- `project`: huggingface
	- `trackio_space_id`: trackio
	- `eval_strategy`: no
	- `per_device_eval_batch_size`: 96
	- `prediction_loss_only`: True
	- `eval_on_start`: False
	- `eval_do_concat_batches`: True
	- `eval_use_gather_object`: False
	- `eval_accumulation_steps`: None
	- `include_for_metrics`: []
	- `batch_eval_metrics`: False
	- `save_only_model`: False
	- `save_on_each_node`: False
	- `enable_jit_checkpoint`: False
	- `push_to_hub`: False
	- `hub_private_repo`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_always_push`: False
	- `hub_revision`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `restore_callback_states_from_checkpoint`: False
	- `full_determinism`: False
	- `seed`: 42
	- `data_seed`: None
	- `use_cpu`: False
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `parallelism_config`: None
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `dataloader_prefetch_factor`: None
	- `remove_unused_columns`: True
	- `label_names`: None
	- `train_sampling_strategy`: random
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `ddp_backend`: None
	- `ddp_timeout`: 1800
	- `fsdp`: []
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `deepspeed`: None
	- `debug`: []
	- `skip_memory_metrics`: True
	- `do_predict`: False
	- `resume_from_checkpoint`: None
	- `warmup_ratio`: None
	- `local_rank`: -1
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 2.3923 \| 500 \| 2.2303 \|


	### Framework Versions
	- Python: 3.12.7
	- Sentence Transformers: 5.3.0
	- Transformers: 5.3.0
	- PyTorch: 2.10.0+cu130
	- Accelerate: 1.13.0
	- Datasets: 4.7.0
	- Tokenizers: 0.22.2

	## 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",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{oord2019representationlearningcontrastivepredictive,
	title={Representation Learning with Contrastive Predictive Coding},
	author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
	year={2019},
	eprint={1807.03748},
	archivePrefix={arXiv},
	primaryClass={cs.LG},
	url={https://arxiv.org/abs/1807.03748},
	}
	```

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