Upload embeddinggemma-2-lean sentence-transformers model (finetuned on openai_math 377K dep pairs)

46607a6 verified 25 days ago

17.1 kB

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:346930
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: U card
  sentences:
  - region
  - clique
  - tokens
- source_sentence: branch
  sentences:
  - block Half
  - bool Code
  - schedule
- source_sentence: pair le
  sentences:
  - trans E
  - endpoint
  - selected
- source_sentence: loop Trace
  sentences:
  - blocks
  - potential
  - Is Blowup
- source_sentence: hit weight
  sentences:
  - machine
  - config
  - append
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
model-index:
- name: SentenceTransformer
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: val ir
      type: val_ir
    metrics:
    - type: cosine_accuracy@1
      value: 0.2305
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3385
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.386
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.426
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2305
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11283333333333331
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07719999999999999
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0426
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2305
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3385
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.386
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.426
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3258681126672316
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2940464285714284
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3016717487640095
      name: Cosine Map@100
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 1000000000000000019884624838656 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modalities:** Text, Image, Audio, Video, Message
<!-- - **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'audio': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'architecture': 'EmbeddingGemma2Model'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)
```

## 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
queries = [
    'resolution Cokernel Map comp',
]
documents = [
    'resolution Cokernel Map',
    'potential Of',
    'canonical exposure supported',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.8557, 0.3572, 0.2455]])
```
<!--
### 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: `val_ir`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2305     |
| cosine_accuracy@3   | 0.3385     |
| cosine_accuracy@5   | 0.386      |
| cosine_accuracy@10  | 0.426      |
| cosine_precision@1  | 0.2305     |
| cosine_precision@3  | 0.1128     |
| cosine_precision@5  | 0.0772     |
| cosine_precision@10 | 0.0426     |
| cosine_recall@1     | 0.2305     |
| cosine_recall@3     | 0.3385     |
| cosine_recall@5     | 0.386      |
| cosine_recall@10    | 0.426      |
| **cosine_ndcg@10**  | **0.3259** |
| cosine_mrr@10       | 0.294      |
| cosine_map@100      | 0.3017     |

<!--
## 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: 346,930 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 100 samples:
  |          | anchor                                                                           | positive                                                                         |
  |:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type     | string                                                                           | string                                                                           |
  | modality | text                                                                             | text                                                                             |
  | details  | <ul><li>min: 3 tokens</li><li>mean: 6.48 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.38 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
  | anchor                                                     | positive                                  |
  |:-----------------------------------------------------------|:------------------------------------------|
  | <code>resolution Cokernel Map comp</code>                  | <code>resolution Cokernel Map</code>      |
  | <code>potential Of continuous</code>                       | <code>potential Of</code>                 |
  | <code>sum fully exposed code success probability le</code> | <code>canonical exposure supported</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`: 256
- `num_train_epochs`: 2
- `warmup_steps`: 200
- `bf16`: True

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

- `per_device_train_batch_size`: 256
- `num_train_epochs`: 2
- `max_steps`: -1
- `learning_rate`: 5e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 200
- `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`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `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`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 8
- `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
- `dataloader_multiprocessing_context`: None
- `dataloader_in_order`: True
- `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_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
- `warmup_ratio`: None

</details>

### Training Logs
| Epoch  | Step | Training Loss | val_ir_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------------:|
| 0.0737 | 100  | 3.5315        | -                     |
| 0.1475 | 200  | 2.8227        | -                     |
| 0.2212 | 300  | 2.6758        | -                     |
| 0.2950 | 400  | 2.5389        | -                     |
| 0.3687 | 500  | 2.4665        | -                     |
| 0.4425 | 600  | 2.3844        | -                     |
| 0.5162 | 700  | 2.3230        | -                     |
| 0.5900 | 800  | 2.2864        | -                     |
| 0.6637 | 900  | 2.2415        | -                     |
| 0.7375 | 1000 | 2.1594        | 0.3263                |
| 0.8112 | 1100 | 2.1646        | -                     |
| 0.8850 | 1200 | 2.1441        | -                     |
| 0.9587 | 1300 | 2.1340        | -                     |
| 1.0324 | 1400 | 1.9848        | -                     |
| 1.1062 | 1500 | 1.8459        | -                     |
| 1.1799 | 1600 | 1.8273        | -                     |
| 1.2537 | 1700 | 1.8094        | -                     |
| 1.3274 | 1800 | 1.7973        | -                     |
| 1.4012 | 1900 | 1.8163        | -                     |
| 1.4749 | 2000 | 1.7998        | 0.3268                |
| 1.5487 | 2100 | 1.8070        | -                     |
| 1.6224 | 2200 | 1.8098        | -                     |
| 1.6962 | 2300 | 1.7844        | -                     |
| 1.7699 | 2400 | 1.7933        | -                     |
| 1.8437 | 2500 | 1.7804        | -                     |
| 1.9174 | 2600 | 1.7810        | -                     |
| 1.9912 | 2700 | 1.7693        | -                     |
| 2.0    | 2712 | -             | 0.3259                |


### Training Time
- **Training**: 43.3 minutes
- **Evaluation**: 2.0 minutes
- **Total**: 45.3 minutes

### Framework Versions
- Python: 3.13.2
- Sentence Transformers: 6.1.0
- Transformers: 5.19.0
- PyTorch: 2.11.0+cu128
- Accelerate: 1.15.0
- Datasets: 5.1.0
- Tokenizers: 0.23.3

## Additional Resources

- [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models.
- [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss.
- [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors.
- [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API.
- [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough.

## 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:346930
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: U card
	sentences:
	- region
	- clique
	- tokens
	- source_sentence: branch
	sentences:
	- block Half
	- bool Code
	- schedule
	- source_sentence: pair le
	sentences:
	- trans E
	- endpoint
	- selected
	- source_sentence: loop Trace
	sentences:
	- blocks
	- potential
	- Is Blowup
	- source_sentence: hit weight
	sentences:
	- machine
	- config
	- append
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	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
	model-index:
	- name: SentenceTransformer
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: val ir
	type: val_ir
	metrics:
	- type: cosine_accuracy@1
	value: 0.2305
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.3385
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.386
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.426
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.2305
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.11283333333333331
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.07719999999999999
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0426
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.2305
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.3385
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.386
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.426
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.3258681126672316
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.2940464285714284
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.3016717487640095
	name: Cosine Map@100
	---

	# SentenceTransformer

	This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	<!-- - Base model: [Unknown](https://huggingface.co/unknown) -->
	- Maximum Sequence Length: 1000000000000000019884624838656 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Supported Modalities: Text, Image, Audio, Video, Message
	<!-- - 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'audio': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'architecture': 'EmbeddingGemma2Model'})
	(1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
	(2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
	)
	```

	## 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
	queries = [
	'resolution Cokernel Map comp',
	]
	documents = [
	'resolution Cokernel Map',
	'potential Of',
	'canonical exposure supported',
	]
	query_embeddings = model.encode_query(queries)
	document_embeddings = model.encode_document(documents)
	print(query_embeddings.shape, document_embeddings.shape)
	# [1, 768] [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(query_embeddings, document_embeddings)
	print(similarities)
	# tensor([[0.8557, 0.3572, 0.2455]])
	```
	<!--
	### 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: `val_ir`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.2305 \|
	\| cosine_accuracy@3 \| 0.3385 \|
	\| cosine_accuracy@5 \| 0.386 \|
	\| cosine_accuracy@10 \| 0.426 \|
	\| cosine_precision@1 \| 0.2305 \|
	\| cosine_precision@3 \| 0.1128 \|
	\| cosine_precision@5 \| 0.0772 \|
	\| cosine_precision@10 \| 0.0426 \|
	\| cosine_recall@1 \| 0.2305 \|
	\| cosine_recall@3 \| 0.3385 \|
	\| cosine_recall@5 \| 0.386 \|
	\| cosine_recall@10 \| 0.426 \|
	\| cosine_ndcg@10 \| 0.3259 \|
	\| cosine_mrr@10 \| 0.294 \|
	\| cosine_map@100 \| 0.3017 \|

	<!--
	## 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: 346,930 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 100 samples:
	\| \| anchor \| positive \|
	\|:---------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| modality \| text \| text \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 6.48 tokens</li><li>max: 11 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 5.38 tokens</li><li>max: 11 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:-----------------------------------------------------------\|:------------------------------------------\|
	\| <code>resolution Cokernel Map comp</code> \| <code>resolution Cokernel Map</code> \|
	\| <code>potential Of continuous</code> \| <code>potential Of</code> \|
	\| <code>sum fully exposed code success probability le</code> \| <code>canonical exposure supported</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`: 256
	- `num_train_epochs`: 2
	- `warmup_steps`: 200
	- `bf16`: True

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

	- `per_device_train_batch_size`: 256
	- `num_train_epochs`: 2
	- `max_steps`: -1
	- `learning_rate`: 5e-05
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: None
	- `warmup_steps`: 200
	- `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`: 1.0
	- `label_smoothing_factor`: 0.0
	- `bf16`: True
	- `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`: None
	- `trackio_bucket_id`: None
	- `trackio_static_space_id`: None
	- `per_device_eval_batch_size`: 8
	- `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
	- `dataloader_multiprocessing_context`: None
	- `dataloader_in_order`: True
	- `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_static_graph`: None
	- `ddp_backend`: None
	- `ddp_timeout`: 1800
	- `fsdp`: None
	- `fsdp_config`: None
	- `deepspeed`: None
	- `debug`: []
	- `skip_memory_metrics`: True
	- `do_predict`: False
	- `resume_from_checkpoint`: None
	- `local_rank`: -1
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}
	- `warmup_ratio`: None

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| val_ir_cosine_ndcg@10 \|
	\|:------:\|:----:\|:-------------:\|:---------------------:\|
	\| 0.0737 \| 100 \| 3.5315 \| - \|
	\| 0.1475 \| 200 \| 2.8227 \| - \|
	\| 0.2212 \| 300 \| 2.6758 \| - \|
	\| 0.2950 \| 400 \| 2.5389 \| - \|
	\| 0.3687 \| 500 \| 2.4665 \| - \|
	\| 0.4425 \| 600 \| 2.3844 \| - \|
	\| 0.5162 \| 700 \| 2.3230 \| - \|
	\| 0.5900 \| 800 \| 2.2864 \| - \|
	\| 0.6637 \| 900 \| 2.2415 \| - \|
	\| 0.7375 \| 1000 \| 2.1594 \| 0.3263 \|
	\| 0.8112 \| 1100 \| 2.1646 \| - \|
	\| 0.8850 \| 1200 \| 2.1441 \| - \|
	\| 0.9587 \| 1300 \| 2.1340 \| - \|
	\| 1.0324 \| 1400 \| 1.9848 \| - \|
	\| 1.1062 \| 1500 \| 1.8459 \| - \|
	\| 1.1799 \| 1600 \| 1.8273 \| - \|
	\| 1.2537 \| 1700 \| 1.8094 \| - \|
	\| 1.3274 \| 1800 \| 1.7973 \| - \|
	\| 1.4012 \| 1900 \| 1.8163 \| - \|
	\| 1.4749 \| 2000 \| 1.7998 \| 0.3268 \|
	\| 1.5487 \| 2100 \| 1.8070 \| - \|
	\| 1.6224 \| 2200 \| 1.8098 \| - \|
	\| 1.6962 \| 2300 \| 1.7844 \| - \|
	\| 1.7699 \| 2400 \| 1.7933 \| - \|
	\| 1.8437 \| 2500 \| 1.7804 \| - \|
	\| 1.9174 \| 2600 \| 1.7810 \| - \|
	\| 1.9912 \| 2700 \| 1.7693 \| - \|
	\| 2.0 \| 2712 \| - \| 0.3259 \|


	### Training Time
	- Training: 43.3 minutes
	- Evaluation: 2.0 minutes
	- Total: 45.3 minutes

	### Framework Versions
	- Python: 3.13.2
	- Sentence Transformers: 6.1.0
	- Transformers: 5.19.0
	- PyTorch: 2.11.0+cu128
	- Accelerate: 1.15.0
	- Datasets: 5.1.0
	- Tokenizers: 0.23.3

	## Additional Resources

	- [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models.
	- [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss.
	- [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors.
	- [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API.
	- [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough.

	## 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.
	-->