Add new SentenceTransformer model

4ef3c65 verified 20 days ago

26.5 kB

	---
	language:
	- en
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- loss:MultipleNegativesRankingLoss
	base_model: Snowflake/snowflake-arctic-embed-l-v2.0
	widget:
	- source_sentence: A construction worker is standing on a crane placing a large arm
	on top of a stature in progress.
	sentences:
	- A man is playing with his camera.
	- A person standing
	- Nobody is standing
	- source_sentence: A boy in red slides down an inflatable ride.
	sentences:
	- a baby smiling
	- A boy is playing on an inflatable ride.
	- A boy pierces a knife through an inflatable ride.
	- source_sentence: A man in a black shirt is playing a guitar.
	sentences:
	- A group of women are selling their wares
	- The man is wearing black.
	- The man is wearing a blue shirt.
	- source_sentence: A man with a large power drill standing next to his daughter with
	a vacuum cleaner hose.
	sentences:
	- A man holding a drill stands next to a girl holding a vacuum hose.
	- Kids ride an amusement ride.
	- The man and girl are painting the walls.
	- source_sentence: A middle-aged man works under the engine of a train on rail tracks.
	sentences:
	- A guy is working on a train.
	- Two young asian men are squatting.
	- A guy is driving to work.
	datasets:
	- sentence-transformers/all-nli
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	model-index:
	- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: all nli test
	type: all-nli-test
	metrics:
	- type: cosine_accuracy
	value: 0.9558178241791496
	name: Cosine Accuracy
	---

	# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co./Snowflake/snowflake-arctic-embed-l-v2.0) on the [all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-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: [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co./Snowflake/snowflake-arctic-embed-l-v2.0) <!-- at revision 7f311bb640ad3babc0a4e3a8873240dcba44c9d2 -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli)
	- Language: en
	<!-- - License: Unknown -->

	### 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
	(1): Pooling({'word_embedding_dimension': 1024, '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("JatinkInnovision/snowflake-arctic-embed-l-v2.0_all-nli")
	# Run inference
	sentences = [
	'A middle-aged man works under the engine of a train on rail tracks.',
	'A guy is working on a train.',
	'A guy is driving to work.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

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	## Evaluation

	### Metrics

	#### Triplet

	* Dataset: `all-nli-test`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy \| 0.9558 \|

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	## Training Details

	### Training Dataset

	#### all-nli

	* Dataset: [all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 557,850 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 10.9 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 42 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.76 tokens</li><li>max: 55 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</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"
	}
	```

	### Evaluation Dataset

	#### all-nli

	* Dataset: [all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 6,584 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 20.31 tokens</li><li>max: 83 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 35 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 32 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \|
	\| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</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"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 50
	- `per_device_eval_batch_size`: 50
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: 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`: 50
	- `per_device_eval_batch_size`: 50
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-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
	- `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`: None
	- `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`: False
	- `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
	- `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`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `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
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| Validation Loss \| all-nli-test_cosine_accuracy \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:----------------------------:\|
	\| 0.0090 \| 100 \| 1.8838 \| 0.6502 \| - \|
	\| 0.0179 \| 200 \| 1.2991 \| 0.6177 \| - \|
	\| 0.0269 \| 300 \| 1.2721 \| 0.6417 \| - \|
	\| 0.0359 \| 400 \| 1.2265 \| 0.7053 \| - \|
	\| 0.0448 \| 500 \| 1.0111 \| 0.7147 \| - \|
	\| 0.0538 \| 600 \| 1.0491 \| 0.7457 \| - \|
	\| 0.0627 \| 700 \| 1.0186 \| 0.7922 \| - \|
	\| 0.0717 \| 800 \| 1.135 \| 0.8940 \| - \|
	\| 0.0807 \| 900 \| 1.0747 \| 0.7007 \| - \|
	\| 0.0896 \| 1000 \| 0.9373 \| 0.7298 \| - \|
	\| 0.0986 \| 1100 \| 0.9572 \| 0.6809 \| - \|
	\| 0.1076 \| 1200 \| 1.1316 \| 0.7260 \| - \|
	\| 0.1165 \| 1300 \| 0.9188 \| 0.7085 \| - \|
	\| 0.1255 \| 1400 \| 0.9554 \| 0.6876 \| - \|
	\| 0.1344 \| 1500 \| 0.9494 \| 0.7492 \| - \|
	\| 0.1434 \| 1600 \| 0.811 \| 0.7234 \| - \|
	\| 0.1524 \| 1700 \| 0.7766 \| 0.6744 \| - \|
	\| 0.1613 \| 1800 \| 0.9317 \| 0.7178 \| - \|
	\| 0.1703 \| 1900 \| 0.9148 \| 0.6960 \| - \|
	\| 0.1793 \| 2000 \| 0.8643 \| 0.6642 \| - \|
	\| 0.1882 \| 2100 \| 0.7604 \| 0.6425 \| - \|
	\| 0.1972 \| 2200 \| 0.776 \| 0.6347 \| - \|
	\| 0.2061 \| 2300 \| 0.8286 \| 0.6581 \| - \|
	\| 0.2151 \| 2400 \| 0.8946 \| 0.5866 \| - \|
	\| 0.2241 \| 2500 \| 0.8507 \| 0.6845 \| - \|
	\| 0.2330 \| 2600 \| 0.7917 \| 0.6091 \| - \|
	\| 0.2420 \| 2700 \| 0.8192 \| 0.7073 \| - \|
	\| 0.2510 \| 2800 \| 0.8818 \| 0.6584 \| - \|
	\| 0.2599 \| 2900 \| 0.8261 \| 0.6112 \| - \|
	\| 0.2689 \| 3000 \| 0.8017 \| 0.6883 \| - \|
	\| 0.2779 \| 3100 \| 0.8147 \| 0.6450 \| - \|
	\| 0.2868 \| 3200 \| 0.8297 \| 0.6086 \| - \|
	\| 0.2958 \| 3300 \| 0.7516 \| 0.5857 \| - \|
	\| 0.3047 \| 3400 \| 0.8628 \| 0.6061 \| - \|
	\| 0.3137 \| 3500 \| 0.7758 \| 0.5751 \| - \|
	\| 0.3227 \| 3600 \| 0.7773 \| 0.6022 \| - \|
	\| 0.3316 \| 3700 \| 0.7559 \| 0.5446 \| - \|
	\| 0.3406 \| 3800 \| 0.796 \| 0.5842 \| - \|
	\| 0.3496 \| 3900 \| 0.8295 \| 0.5822 \| - \|
	\| 0.3585 \| 4000 \| 0.7292 \| 0.5821 \| - \|
	\| 0.3675 \| 4100 \| 0.7475 \| 0.6358 \| - \|
	\| 0.3764 \| 4200 \| 0.7916 \| 0.5688 \| - \|
	\| 0.3854 \| 4300 \| 0.7214 \| 0.5653 \| - \|
	\| 0.3944 \| 4400 \| 0.704 \| 0.5564 \| - \|
	\| 0.4033 \| 4500 \| 0.7817 \| 0.5876 \| - \|
	\| 0.4123 \| 4600 \| 0.7549 \| 0.5358 \| - \|
	\| 0.4213 \| 4700 \| 0.7206 \| 0.5785 \| - \|
	\| 0.4302 \| 4800 \| 0.7462 \| 0.5568 \| - \|
	\| 0.4392 \| 4900 \| 0.665 \| 0.5765 \| - \|
	\| 0.4481 \| 5000 \| 0.7743 \| 0.5303 \| - \|
	\| 0.4571 \| 5100 \| 0.7055 \| 0.5733 \| - \|
	\| 0.4661 \| 5200 \| 0.7004 \| 0.6280 \| - \|
	\| 0.4750 \| 5300 \| 0.7021 \| 0.5444 \| - \|
	\| 0.4840 \| 5400 \| 0.6858 \| 0.5787 \| - \|
	\| 0.4930 \| 5500 \| 0.7007 \| 0.6124 \| - \|
	\| 0.5019 \| 5600 \| 0.6722 \| 0.5705 \| - \|
	\| 0.5109 \| 5700 \| 0.7124 \| 0.5440 \| - \|
	\| 0.5199 \| 5800 \| 0.6657 \| 0.5262 \| - \|
	\| 0.5288 \| 5900 \| 0.6784 \| 0.5400 \| - \|
	\| 0.5378 \| 6000 \| 0.6644 \| 0.5093 \| - \|
	\| 0.5467 \| 6100 \| 0.7195 \| 0.5453 \| - \|
	\| 0.5557 \| 6200 \| 0.6958 \| 0.5216 \| - \|
	\| 0.5647 \| 6300 \| 0.7202 \| 0.5250 \| - \|
	\| 0.5736 \| 6400 \| 0.6921 \| 0.5089 \| - \|
	\| 0.5826 \| 6500 \| 0.6926 \| 0.5207 \| - \|
	\| 0.5916 \| 6600 \| 0.714 \| 0.5084 \| - \|
	\| 0.6005 \| 6700 \| 0.6605 \| 0.4943 \| - \|
	\| 0.6095 \| 6800 \| 0.7222 \| 0.5058 \| - \|
	\| 0.6184 \| 6900 \| 0.7171 \| 0.4950 \| - \|
	\| 0.6274 \| 7000 \| 0.6344 \| 0.5110 \| - \|
	\| 0.6364 \| 7100 \| 0.7057 \| 0.5197 \| - \|
	\| 0.6453 \| 7200 \| 0.6895 \| 0.5096 \| - \|
	\| 0.6543 \| 7300 \| 0.7226 \| 0.4819 \| - \|
	\| 0.6633 \| 7400 \| 0.6725 \| 0.4780 \| - \|
	\| 0.6722 \| 7500 \| 0.7469 \| 0.5145 \| - \|
	\| 0.6812 \| 7600 \| 0.7016 \| 0.4969 \| - \|
	\| 0.6901 \| 7700 \| 0.6655 \| 0.4965 \| - \|
	\| 0.6991 \| 7800 \| 0.7281 \| 0.4913 \| - \|
	\| 0.7081 \| 7900 \| 0.6748 \| 0.5121 \| - \|
	\| 0.7170 \| 8000 \| 0.6505 \| 0.5207 \| - \|
	\| 0.7260 \| 8100 \| 0.6594 \| 0.4823 \| - \|
	\| 0.7350 \| 8200 \| 0.7042 \| 0.4903 \| - \|
	\| 0.7439 \| 8300 \| 0.6995 \| 0.4630 \| - \|
	\| 0.7529 \| 8400 \| 0.634 \| 0.4217 \| - \|
	\| 0.7619 \| 8500 \| 0.3772 \| 0.3684 \| - \|
	\| 0.7708 \| 8600 \| 0.3416 \| 0.3585 \| - \|
	\| 0.7798 \| 8700 \| 0.3113 \| 0.3471 \| - \|
	\| 0.7887 \| 8800 \| 0.2793 \| 0.3379 \| - \|
	\| 0.7977 \| 8900 \| 0.2577 \| 0.3349 \| - \|
	\| 0.8067 \| 9000 \| 0.249 \| 0.3320 \| - \|
	\| 0.8156 \| 9100 \| 0.2191 \| 0.3290 \| - \|
	\| 0.8246 \| 9200 \| 0.2492 \| 0.3255 \| - \|
	\| 0.8336 \| 9300 \| 0.2464 \| 0.3258 \| - \|
	\| 0.8425 \| 9400 \| 0.2288 \| 0.3247 \| - \|
	\| 0.8515 \| 9500 \| 0.2132 \| 0.3248 \| - \|
	\| 0.8604 \| 9600 \| 0.2173 \| 0.3259 \| - \|
	\| 0.8694 \| 9700 \| 0.2008 \| 0.3223 \| - \|
	\| 0.8784 \| 9800 \| 0.2016 \| 0.3219 \| - \|
	\| 0.8873 \| 9900 \| 0.1962 \| 0.3195 \| - \|
	\| 0.8963 \| 10000 \| 0.1952 \| 0.3185 \| - \|
	\| 0.9053 \| 10100 \| 0.1959 \| 0.3158 \| - \|
	\| 0.9142 \| 10200 \| 0.2002 \| 0.3138 \| - \|
	\| 0.9232 \| 10300 \| 0.1882 \| 0.3150 \| - \|
	\| 0.9322 \| 10400 \| 0.1856 \| 0.3124 \| - \|
	\| 0.9411 \| 10500 \| 0.1971 \| 0.3143 \| - \|
	\| 0.9501 \| 10600 \| 0.1918 \| 0.3137 \| - \|
	\| 0.9590 \| 10700 \| 0.1825 \| 0.3147 \| - \|
	\| 0.9680 \| 10800 \| 0.1762 \| 0.3155 \| - \|
	\| 0.9770 \| 10900 \| 0.1778 \| 0.3139 \| - \|
	\| 0.9859 \| 11000 \| 0.1659 \| 0.3138 \| - \|
	\| 0.9949 \| 11100 \| 0.1848 \| 0.3131 \| - \|
	\| 1.0 \| 11157 \| - \| - \| 0.9558 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.1
	- Transformers: 4.47.1
	- PyTorch: 2.5.1+cu121
	- Accelerate: 1.2.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## 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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