Add new SentenceTransformer model

d6a46b5 verified 3 months ago

29.8 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:10000
	- loss:MultipleNegativesRankingLoss
	base_model: google/siglip-base-patch16-384
	widget:
	- source_sentence: A man standing next to a little girl riding a horse.
	sentences:
	- The woman is working on her computer at the desk.
	- A young man holding an umbrella next to a herd of cattle.
	- 'a person sitting at a desk with a keyboard and monitor '
	- source_sentence: 'A car at an intersection while a man is crossing the street. '
	sentences:
	- A plane that is flying in the air.
	- a small girl sitting on a chair holding a white bear
	- A young toddler walks across the grass in a park.
	- source_sentence: A lady riding her bicycle on the side of a street.
	sentences:
	- Flowers hang from a small decorative post in a yard.
	- Flowers in a clear vase sitting on a table.
	- The toilet is near the door in the bathroom.
	- source_sentence: 'A group of zebras standing beside each other in the desert. '
	sentences:
	- The bathroom is clean and ready for us to use.
	- A woman throwing a frisbee as a child looks on.
	- a bird with a pink eye is sitting on a branch in the woods.
	- source_sentence: A large desk by a window is neatly arranged.
	sentences:
	- An old toilet sits in dirt with a helmet on top.
	- A lady sitting at an enormous dining table with lots of food.
	- A long hot dog on a plate on a table.
	datasets:
	- jxie/coco_captions
	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
	co2_eq_emissions:
	emissions: 8.941674503680932
	energy_consumed: 0.03341158239487386
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.123
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: Google SigLIP (384x384 resolution) model trained on COCO Captions
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: coco eval
	type: coco-eval
	metrics:
	- type: cosine_accuracy@1
	value: 0.754
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.942
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.981
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.991
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.754
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.31399999999999995
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19620000000000004
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09910000000000002
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.754
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.942
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.981
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.991
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8846483241893175
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8489523809523812
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8492811828305821
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: coco test
	type: coco-test
	metrics:
	- type: cosine_accuracy@1
	value: 0.765
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.934
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.967
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.992
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.765
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.31133333333333324
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19340000000000004
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09920000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.765
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.934
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.967
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.992
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8877828740849488
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8532043650793657
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8535366959866338
	name: Cosine Map@100
	---

	# Google SigLIP (384x384 resolution) model trained on COCO Captions

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/siglip-base-patch16-384](https://huggingface.co/google/siglip-base-patch16-384) on the [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) dataset. It maps sentences & paragraphs to a None-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: [google/siglip-base-patch16-384](https://huggingface.co/google/siglip-base-patch16-384) <!-- at revision 41aec1c83b32e0a6fca20ad88ba058aa5b5ea394 -->
	- Maximum Sequence Length: None tokens
	- Output Dimensionality: None dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [coco_captions](https://huggingface.co/datasets/jxie/coco_captions)
	- 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': None}, 'image': {'method': 'get_image_features', 'method_output_name': None}}, 'module_output_name': 'sentence_embedding', 'architecture': 'SiglipModel'})
	)
	```

	## 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("tomaarsen/google-siglip-base-384-coco")
	# Run inference
	sentences = [
	'A large desk by a window is neatly arranged.',
	'A long hot dog on a plate on a table.',
	'A lady sitting at an enormous dining table with lots of food.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities)
	# tensor([[1.0000, 0.1984, 0.1492],
	# [0.1984, 1.0000, 0.4638],
	# [0.1492, 0.4638, 1.0000]])
	```

	<!--
	### Direct Usage (Transformers)

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	</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.
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	## Evaluation

	### Metrics

	#### Information Retrieval

	* Datasets: `coco-eval` and `coco-test`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| coco-eval \| coco-test \|
	\|:--------------------\|:-----------\|:-----------\|
	\| cosine_accuracy@1 \| 0.754 \| 0.765 \|
	\| cosine_accuracy@3 \| 0.942 \| 0.934 \|
	\| cosine_accuracy@5 \| 0.981 \| 0.967 \|
	\| cosine_accuracy@10 \| 0.991 \| 0.992 \|
	\| cosine_precision@1 \| 0.754 \| 0.765 \|
	\| cosine_precision@3 \| 0.314 \| 0.3113 \|
	\| cosine_precision@5 \| 0.1962 \| 0.1934 \|
	\| cosine_precision@10 \| 0.0991 \| 0.0992 \|
	\| cosine_recall@1 \| 0.754 \| 0.765 \|
	\| cosine_recall@3 \| 0.942 \| 0.934 \|
	\| cosine_recall@5 \| 0.981 \| 0.967 \|
	\| cosine_recall@10 \| 0.991 \| 0.992 \|
	\| cosine_ndcg@10 \| 0.8846 \| 0.8878 \|
	\| cosine_mrr@10 \| 0.849 \| 0.8532 \|
	\| cosine_map@100 \| 0.8493 \| 0.8535 \|

	<!--
	## 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

	#### coco_captions

	* Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec)
	* Size: 10,000 training samples
	* Columns: <code>image</code> and <code>caption</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| image \| caption \|
	\|:--------\|:----------------------------------\|:------------------------------------------------------------------------------------------------\|
	\| type \| PIL.JpegImagePlugin.JpegImageFile \| string \|
	\| details \| <ul><li></li></ul> \| <ul><li>min: 28 characters</li><li>mean: 52.56 characters</li><li>max: 156 characters</li></ul> \|
	* Samples:
	\| image \| caption \|
	\|:----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------\|
	\| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x20B2C596E90></code> \| <code>A woman wearing a net on her head cutting a cake. </code> \|
	\| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x20B35438610></code> \| <code>A woman cutting a large white sheet cake.</code> \|
	\| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x20B36D74350></code> \| <code>A woman wearing a hair net cutting a large sheet cake.</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
	}
	```

	### Evaluation Dataset

	#### coco_captions

	* Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec)
	* Size: 1,000 evaluation samples
	* Columns: <code>image</code> and <code>caption</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| image \| caption \|
	\|:--------\|:----------------------------------\|:------------------------------------------------------------------------------------------------\|
	\| type \| PIL.JpegImagePlugin.JpegImageFile \| string \|
	\| details \| <ul><li></li></ul> \| <ul><li>min: 27 characters</li><li>mean: 52.45 characters</li><li>max: 151 characters</li></ul> \|
	* Samples:
	\| image \| caption \|
	\|:----------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x20B36C2CF90></code> \| <code>A child holding a flowered umbrella and petting a yak.</code> \|
	\| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x20B34D10C50></code> \| <code>A young man holding an umbrella next to a herd of cattle.</code> \|
	\| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x20B36D7DC90></code> \| <code>a young boy barefoot holding an umbrella touching the horn of a cow</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
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_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`: 1
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `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
	- `use_cpu`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `bf16`: True
	- `fp16`: False
	- `half_precision_backend`: None
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `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`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch_fused
	- `optim_args`: None
	- `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`: None
	- `hub_always_push`: False
	- `hub_revision`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: no
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: True
	- `prompts`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| coco-eval_cosine_ndcg@10 \| coco-test_cosine_ndcg@10 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:------------------------:\|:------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.2226 \| - \|
	\| 0.0112 \| 7 \| 2.7011 \| - \| - \| - \|
	\| 0.0224 \| 14 \| 3.1603 \| - \| - \| - \|
	\| 0.0336 \| 21 \| 3.1235 \| - \| - \| - \|
	\| 0.0448 \| 28 \| 2.5265 \| - \| - \| - \|
	\| 0.056 \| 35 \| 2.5207 \| - \| - \| - \|
	\| 0.0672 \| 42 \| 2.3686 \| - \| - \| - \|
	\| 0.0784 \| 49 \| 1.5387 \| - \| - \| - \|
	\| 0.0896 \| 56 \| 1.4576 \| - \| - \| - \|
	\| 0.1008 \| 63 \| 1.553 \| 0.8158 \| 0.7010 \| - \|
	\| 0.112 \| 70 \| 1.0186 \| - \| - \| - \|
	\| 0.1232 \| 77 \| 0.618 \| - \| - \| - \|
	\| 0.1344 \| 84 \| 0.6102 \| - \| - \| - \|
	\| 0.1456 \| 91 \| 0.4724 \| - \| - \| - \|
	\| 0.1568 \| 98 \| 0.5023 \| - \| - \| - \|
	\| 0.168 \| 105 \| 0.4495 \| - \| - \| - \|
	\| 0.1792 \| 112 \| 0.4106 \| - \| - \| - \|
	\| 0.1904 \| 119 \| 0.3623 \| - \| - \| - \|
	\| 0.2016 \| 126 \| 0.282 \| 0.3537 \| 0.8117 \| - \|
	\| 0.2128 \| 133 \| 0.3217 \| - \| - \| - \|
	\| 0.224 \| 140 \| 0.1981 \| - \| - \| - \|
	\| 0.2352 \| 147 \| 0.2619 \| - \| - \| - \|
	\| 0.2464 \| 154 \| 0.3123 \| - \| - \| - \|
	\| 0.2576 \| 161 \| 0.2774 \| - \| - \| - \|
	\| 0.2688 \| 168 \| 0.3604 \| - \| - \| - \|
	\| 0.28 \| 175 \| 0.211 \| - \| - \| - \|
	\| 0.2912 \| 182 \| 0.1822 \| - \| - \| - \|
	\| 0.3024 \| 189 \| 0.199 \| 0.2739 \| 0.8373 \| - \|
	\| 0.3136 \| 196 \| 0.2138 \| - \| - \| - \|
	\| 0.3248 \| 203 \| 0.1705 \| - \| - \| - \|
	\| 0.336 \| 210 \| 0.2555 \| - \| - \| - \|
	\| 0.3472 \| 217 \| 0.1738 \| - \| - \| - \|
	\| 0.3584 \| 224 \| 0.2214 \| - \| - \| - \|
	\| 0.3696 \| 231 \| 0.2284 \| - \| - \| - \|
	\| 0.3808 \| 238 \| 0.1638 \| - \| - \| - \|
	\| 0.392 \| 245 \| 0.2248 \| - \| - \| - \|
	\| 0.4032 \| 252 \| 0.2234 \| 0.2361 \| 0.8440 \| - \|
	\| 0.4144 \| 259 \| 0.2131 \| - \| - \| - \|
	\| 0.4256 \| 266 \| 0.2852 \| - \| - \| - \|
	\| 0.4368 \| 273 \| 0.193 \| - \| - \| - \|
	\| 0.448 \| 280 \| 0.1341 \| - \| - \| - \|
	\| 0.4592 \| 287 \| 0.1871 \| - \| - \| - \|
	\| 0.4704 \| 294 \| 0.0927 \| - \| - \| - \|
	\| 0.4816 \| 301 \| 0.1118 \| - \| - \| - \|
	\| 0.4928 \| 308 \| 0.1321 \| - \| - \| - \|
	\| 0.504 \| 315 \| 0.1706 \| 0.2286 \| 0.8624 \| - \|
	\| 0.5152 \| 322 \| 0.259 \| - \| - \| - \|
	\| 0.5264 \| 329 \| 0.1651 \| - \| - \| - \|
	\| 0.5376 \| 336 \| 0.1935 \| - \| - \| - \|
	\| 0.5488 \| 343 \| 0.1076 \| - \| - \| - \|
	\| 0.56 \| 350 \| 0.1974 \| - \| - \| - \|
	\| 0.5712 \| 357 \| 0.1411 \| - \| - \| - \|
	\| 0.5824 \| 364 \| 0.2281 \| - \| - \| - \|
	\| 0.5936 \| 371 \| 0.0854 \| - \| - \| - \|
	\| 0.6048 \| 378 \| 0.139 \| 0.2097 \| 0.8671 \| - \|
	\| 0.616 \| 385 \| 0.1534 \| - \| - \| - \|
	\| 0.6272 \| 392 \| 0.1449 \| - \| - \| - \|
	\| 0.6384 \| 399 \| 0.1692 \| - \| - \| - \|
	\| 0.6496 \| 406 \| 0.0753 \| - \| - \| - \|
	\| 0.6608 \| 413 \| 0.1212 \| - \| - \| - \|
	\| 0.672 \| 420 \| 0.1508 \| - \| - \| - \|
	\| 0.6832 \| 427 \| 0.1738 \| - \| - \| - \|
	\| 0.6944 \| 434 \| 0.1549 \| - \| - \| - \|
	\| 0.7056 \| 441 \| 0.2302 \| 0.2139 \| 0.8679 \| - \|
	\| 0.7168 \| 448 \| 0.1492 \| - \| - \| - \|
	\| 0.728 \| 455 \| 0.1438 \| - \| - \| - \|
	\| 0.7392 \| 462 \| 0.109 \| - \| - \| - \|
	\| 0.7504 \| 469 \| 0.1419 \| - \| - \| - \|
	\| 0.7616 \| 476 \| 0.1404 \| - \| - \| - \|
	\| 0.7728 \| 483 \| 0.1506 \| - \| - \| - \|
	\| 0.784 \| 490 \| 0.1082 \| - \| - \| - \|
	\| 0.7952 \| 497 \| 0.1568 \| - \| - \| - \|
	\| 0.8064 \| 504 \| 0.1336 \| 0.1895 \| 0.8853 \| - \|
	\| 0.8176 \| 511 \| 0.15 \| - \| - \| - \|
	\| 0.8288 \| 518 \| 0.1508 \| - \| - \| - \|
	\| 0.84 \| 525 \| 0.1053 \| - \| - \| - \|
	\| 0.8512 \| 532 \| 0.1173 \| - \| - \| - \|
	\| 0.8624 \| 539 \| 0.0883 \| - \| - \| - \|
	\| 0.8736 \| 546 \| 0.1023 \| - \| - \| - \|
	\| 0.8848 \| 553 \| 0.0647 \| - \| - \| - \|
	\| 0.896 \| 560 \| 0.0697 \| - \| - \| - \|
	\| 0.9072 \| 567 \| 0.143 \| 0.1840 \| 0.8846 \| - \|
	\| 0.9184 \| 574 \| 0.1319 \| - \| - \| - \|
	\| 0.9296 \| 581 \| 0.1341 \| - \| - \| - \|
	\| 0.9408 \| 588 \| 0.1138 \| - \| - \| - \|
	\| 0.952 \| 595 \| 0.1371 \| - \| - \| - \|
	\| 0.9632 \| 602 \| 0.0648 \| - \| - \| - \|
	\| 0.9744 \| 609 \| 0.0609 \| - \| - \| - \|
	\| 0.9856 \| 616 \| 0.1182 \| - \| - \| - \|
	\| 0.9968 \| 623 \| 0.1419 \| - \| - \| - \|
	\| -1 \| -1 \| - \| - \| - \| 0.8878 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.033 kWh
	- Carbon Emitted: 0.009 kg of CO2
	- Hours Used: 0.123 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 5.2.0.dev0
	- Transformers: 4.57.0.dev0
	- PyTorch: 2.8.0+cu128
	- Accelerate: 1.6.0
	- Datasets: 3.6.0
	- Tokenizers: 0.22.1

	## 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",
	}
	```

	#### 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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