ToS-Sentence-Transformers-V3 / README.md

Add new SentenceTransformer model

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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:167508
	- loss:MultipleNegativesRankingLoss
	base_model: sentence-transformers/all-MiniLM-L6-v2
	widget:
	- source_sentence: We operate globally and may transfer your personal information
	sentences:
	- Content you post may be edited by the service for any reason
	- You can retrieve an archive of your data
	- Your data may be processed and stored anywhere in the world
	- source_sentence: These pages, the content, and infrastructure of these pages and
	the online reservation service (including the facilitation of payment service)
	provided by us on these pages and through the website are owned, operated, and
	provided by Booking.com B.V. and are provided for your personal, non-commercial
	(B2C) use only, subject to the terms and conditions set out below.
	sentences:
	- The service will only respond to government requests that are reasonable
	- Two factor authentication is provided for your account
	- This service is only available for use individually and non-commercially.
	- source_sentence: If you do not want to receive email or other communications from
	us, please adjust your Customer Communication Preferences
	sentences:
	- You can opt out of promotional communications
	- This Service provides a list of Third Parties involved in its operation.
	- Terms may be changed at any time
	- source_sentence: irrevocably and unconditionally waive any moral rights or similar
	rights you have in any Content pursuant to the Copyright, Designs and Patents
	Act 1988 (as amended, superseded or replaced from time to time) (the “Act”) or
	equivalent legislation anywhere in the World.
	sentences:
	- User-generated content can be blocked or censored for any reason
	- User suspension from the service will be fair and proportionate.
	- You waive your moral rights
	- source_sentence: You agree that regardless of any statute or law to the contrary,
	any claim or cause of action arising out of or related to use of the Desmos Services
	or these Terms must be filed within one (1) year after such claim or cause of
	action arose or be forever barred.
	sentences:
	- The data retention period is kept to the minimum necessary for fulfilling its
	purposes
	- You are not allowed to use pseudonyms, as trust and transparency between users
	regarding their identities is relevant to the service.
	- You have a reduced time period to take legal action against the service
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	model-index:
	- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: all nli dev
	type: all-nli-dev
	metrics:
	- type: cosine_accuracy
	value: 0.9990686774253845
	name: Cosine Accuracy
	---

	# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
	- 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/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': 256, 'do_lower_case': False}) with Transformer model: 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("AryehRotberg/ToS-Sentence-Transformers-V3")
	# Run inference
	sentences = [
	'You agree that regardless of any statute or law to the contrary, any claim or cause of action arising out of or related to use of the Desmos Services or these Terms must be filed within one (1) year after such claim or cause of action arose or be forever barred.',
	'You have a reduced time period to take legal action against the service',
	'The data retention period is kept to the minimum necessary for fulfilling its purposes',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

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

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

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Triplet

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy \| 0.9991 \|

	<!--
	## 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: 167,508 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: 3 tokens</li><li>mean: 48.65 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.89 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.24 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| <code>This websites also uses Google Analytics, a web analysis service provided by Google Inc. ("Google"). Google Inc. is an enterprise of the holding company Alphabet Inc., domiciled in the USA</code> \| <code>Third-party cookies are used for statistics</code> \| <code>The service is open-source</code> \|
	\| <code>Terms of Use This Agreement was last revised on Dec 6, 2017.</code> \| <code>There is a date of the last update of the agreements</code> \| <code>Many third parties are involved in operating the service</code> \|
	\| <code>We reserve the right, at Our sole discretion, to modify or replace these Terms at any time. If a revision is material We will make reasonable efforts to provide at least 30 days' notice prior to any new terms taking effect.</code> \| <code>When the service wants to make a material change to its terms, you are notified at least 30 days in advance</code> \| <code>User-generated content is encrypted, and this service cannot decrypt it</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

	#### Unnamed Dataset

	* Size: 41,877 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: 4 tokens</li><li>mean: 47.12 tokens</li><li>max: 256 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.92 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.56 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>© access or search or attempt to access or search the Services by any means (automated or otherwise) other than through our currently available, published interfaces that are provided by Podyssey (and only pursuant to those terms and conditions) or unless permitted by Podyssey’s robots.txt file or other robot exclusion mechanisms. (d) scrape the Services, and particularly scrape Content (as defined below) from the Services.</code> \| <code>Spidering, crawling, or accessing the site through any automated means is not allowed</code> \| <code>User-generated content is encrypted, and this service cannot decrypt it</code> \|
	\| <code>License by Customer to Use Feedback. Customer grants to SFDC and its Affiliates a worldwide, perpetual, irrevocable, royalty-free license to use and incorporate into its services any suggestion, enhancement request, recommendation, correction or other feedback provided by Customer or Users relating to the operation of SFDC’s or its Affiliates’ services.</code> \| <code>If you offer suggestions to the service, they may use that without your approval or compensation, but they do not become the owner</code> \| <code>You can opt out of providing personal information to third parties</code> \|
	\| <code>OVPN does not log any activity when connected to our VPN service.</code> \| <code>Only necessary logs are kept by the service to ensure quality</code> \| <code>You agree to defend, indemnify, and hold the service harmless in case of a claim related to your use of the service</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`: 16
	- `per_device_eval_batch_size`: 16
	- `learning_rate`: 2e-05
	- `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`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 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
	- `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}
	- `tp_size`: 0
	- `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
	- `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-dev_cosine_accuracy \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:---------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.9478 \|
	\| 0.0096 \| 100 \| 1.34 \| 1.1442 \| 0.9598 \|
	\| 0.0191 \| 200 \| 1.1161 \| 0.9002 \| 0.9725 \|
	\| 0.0287 \| 300 \| 0.8731 \| 0.7618 \| 0.9786 \|
	\| 0.0382 \| 400 \| 0.739 \| 0.6587 \| 0.9835 \|
	\| 0.0478 \| 500 \| 0.6753 \| 0.5901 \| 0.9860 \|
	\| 0.0573 \| 600 \| 0.6199 \| 0.5277 \| 0.9877 \|
	\| 0.0669 \| 700 \| 0.5434 \| 0.4952 \| 0.9890 \|
	\| 0.0764 \| 800 \| 0.4781 \| 0.4602 \| 0.9901 \|
	\| 0.0860 \| 900 \| 0.4852 \| 0.4351 \| 0.9905 \|
	\| 0.0955 \| 1000 \| 0.4329 \| 0.4114 \| 0.9910 \|
	\| 0.1051 \| 1100 \| 0.4432 \| 0.3804 \| 0.9919 \|
	\| 0.1146 \| 1200 \| 0.4224 \| 0.3649 \| 0.9928 \|
	\| 0.1242 \| 1300 \| 0.3697 \| 0.3488 \| 0.9930 \|
	\| 0.1337 \| 1400 \| 0.3724 \| 0.3338 \| 0.9936 \|
	\| 0.1433 \| 1500 \| 0.3467 \| 0.3246 \| 0.9938 \|
	\| 0.1528 \| 1600 \| 0.3728 \| 0.3045 \| 0.9945 \|
	\| 0.1624 \| 1700 \| 0.3281 \| 0.2952 \| 0.9943 \|
	\| 0.1719 \| 1800 \| 0.3187 \| 0.2907 \| 0.9946 \|
	\| 0.1815 \| 1900 \| 0.336 \| 0.2707 \| 0.9952 \|
	\| 0.1910 \| 2000 \| 0.2957 \| 0.2667 \| 0.9952 \|
	\| 0.2006 \| 2100 \| 0.2787 \| 0.2650 \| 0.9955 \|
	\| 0.2101 \| 2200 \| 0.2698 \| 0.2534 \| 0.9954 \|
	\| 0.2197 \| 2300 \| 0.2741 \| 0.2562 \| 0.9956 \|
	\| 0.2292 \| 2400 \| 0.2736 \| 0.2477 \| 0.9957 \|
	\| 0.2388 \| 2500 \| 0.2936 \| 0.2400 \| 0.9960 \|
	\| 0.2483 \| 2600 \| 0.2513 \| 0.2321 \| 0.9962 \|
	\| 0.2579 \| 2700 \| 0.2564 \| 0.2301 \| 0.9965 \|
	\| 0.2674 \| 2800 \| 0.245 \| 0.2277 \| 0.9965 \|
	\| 0.2770 \| 2900 \| 0.2406 \| 0.2156 \| 0.9967 \|
	\| 0.2865 \| 3000 \| 0.2074 \| 0.2125 \| 0.9966 \|
	\| 0.2961 \| 3100 \| 0.2544 \| 0.2081 \| 0.9965 \|
	\| 0.3056 \| 3200 \| 0.2333 \| 0.2034 \| 0.9968 \|
	\| 0.3152 \| 3300 \| 0.2311 \| 0.1998 \| 0.9971 \|
	\| 0.3247 \| 3400 \| 0.2294 \| 0.1931 \| 0.9972 \|
	\| 0.3343 \| 3500 \| 0.2289 \| 0.1877 \| 0.9973 \|
	\| 0.3438 \| 3600 \| 0.2291 \| 0.1843 \| 0.9974 \|
	\| 0.3534 \| 3700 \| 0.2406 \| 0.1748 \| 0.9977 \|
	\| 0.3629 \| 3800 \| 0.1851 \| 0.1754 \| 0.9974 \|
	\| 0.3725 \| 3900 \| 0.2172 \| 0.1691 \| 0.9976 \|
	\| 0.3820 \| 4000 \| 0.1885 \| 0.1677 \| 0.9979 \|
	\| 0.3916 \| 4100 \| 0.2041 \| 0.1662 \| 0.9977 \|
	\| 0.4011 \| 4200 \| 0.2052 \| 0.1671 \| 0.9977 \|
	\| 0.4107 \| 4300 \| 0.1739 \| 0.1626 \| 0.9980 \|
	\| 0.4202 \| 4400 \| 0.1721 \| 0.1598 \| 0.9979 \|
	\| 0.4298 \| 4500 \| 0.1682 \| 0.1575 \| 0.9980 \|
	\| 0.4394 \| 4600 \| 0.2076 \| 0.1518 \| 0.9980 \|
	\| 0.4489 \| 4700 \| 0.1657 \| 0.1549 \| 0.9978 \|
	\| 0.4585 \| 4800 \| 0.1827 \| 0.1456 \| 0.9981 \|
	\| 0.4680 \| 4900 \| 0.1577 \| 0.1412 \| 0.9984 \|
	\| 0.4776 \| 5000 \| 0.1869 \| 0.1400 \| 0.9983 \|
	\| 0.4871 \| 5100 \| 0.1437 \| 0.1400 \| 0.9983 \|
	\| 0.4967 \| 5200 \| 0.1806 \| 0.1372 \| 0.9982 \|
	\| 0.5062 \| 5300 \| 0.1457 \| 0.1358 \| 0.9982 \|
	\| 0.5158 \| 5400 \| 0.1529 \| 0.1339 \| 0.9983 \|
	\| 0.5253 \| 5500 \| 0.1732 \| 0.1300 \| 0.9982 \|
	\| 0.5349 \| 5600 \| 0.1563 \| 0.1270 \| 0.9984 \|
	\| 0.5444 \| 5700 \| 0.1411 \| 0.1267 \| 0.9985 \|
	\| 0.5540 \| 5800 \| 0.149 \| 0.1270 \| 0.9985 \|
	\| 0.5635 \| 5900 \| 0.1492 \| 0.1264 \| 0.9985 \|
	\| 0.5731 \| 6000 \| 0.1466 \| 0.1200 \| 0.9986 \|
	\| 0.5826 \| 6100 \| 0.1423 \| 0.1190 \| 0.9986 \|
	\| 0.5922 \| 6200 \| 0.1389 \| 0.1204 \| 0.9985 \|
	\| 0.6017 \| 6300 \| 0.1287 \| 0.1153 \| 0.9984 \|
	\| 0.6113 \| 6400 \| 0.1307 \| 0.1139 \| 0.9986 \|
	\| 0.6208 \| 6500 \| 0.1383 \| 0.1129 \| 0.9987 \|
	\| 0.6304 \| 6600 \| 0.1332 \| 0.1105 \| 0.9987 \|
	\| 0.6399 \| 6700 \| 0.1228 \| 0.1090 \| 0.9988 \|
	\| 0.6495 \| 6800 \| 0.119 \| 0.1093 \| 0.9987 \|
	\| 0.6590 \| 6900 \| 0.1459 \| 0.1076 \| 0.9987 \|
	\| 0.6686 \| 7000 \| 0.1162 \| 0.1058 \| 0.9988 \|
	\| 0.6781 \| 7100 \| 0.1105 \| 0.1054 \| 0.9988 \|
	\| 0.6877 \| 7200 \| 0.1379 \| 0.1044 \| 0.9988 \|
	\| 0.6972 \| 7300 \| 0.1555 \| 0.1017 \| 0.9989 \|
	\| 0.7068 \| 7400 \| 0.1471 \| 0.0982 \| 0.9989 \|
	\| 0.7163 \| 7500 \| 0.1308 \| 0.0983 \| 0.9988 \|
	\| 0.7259 \| 7600 \| 0.1095 \| 0.0965 \| 0.9988 \|
	\| 0.7354 \| 7700 \| 0.1321 \| 0.0956 \| 0.9989 \|
	\| 0.7450 \| 7800 \| 0.1108 \| 0.0938 \| 0.9987 \|
	\| 0.7545 \| 7900 \| 0.1151 \| 0.0918 \| 0.9989 \|
	\| 0.7641 \| 8000 \| 0.1179 \| 0.0920 \| 0.9990 \|
	\| 0.7736 \| 8100 \| 0.117 \| 0.0910 \| 0.9991 \|
	\| 0.7832 \| 8200 \| 0.1426 \| 0.0895 \| 0.9989 \|
	\| 0.7927 \| 8300 \| 0.122 \| 0.0891 \| 0.9990 \|
	\| 0.8023 \| 8400 \| 0.1136 \| 0.0888 \| 0.9989 \|
	\| 0.8118 \| 8500 \| 0.0935 \| 0.0882 \| 0.9989 \|
	\| 0.8214 \| 8600 \| 0.1143 \| 0.0872 \| 0.9989 \|
	\| 0.8309 \| 8700 \| 0.0982 \| 0.0873 \| 0.9989 \|
	\| 0.8405 \| 8800 \| 0.1171 \| 0.0857 \| 0.9989 \|
	\| 0.8500 \| 8900 \| 0.1091 \| 0.0844 \| 0.9989 \|
	\| 0.8596 \| 9000 \| 0.1046 \| 0.0840 \| 0.9989 \|
	\| 0.8691 \| 9100 \| 0.0897 \| 0.0836 \| 0.9990 \|
	\| 0.8787 \| 9200 \| 0.0804 \| 0.0832 \| 0.9991 \|
	\| 0.8883 \| 9300 \| 0.0967 \| 0.0827 \| 0.9991 \|
	\| 0.8978 \| 9400 \| 0.0897 \| 0.0820 \| 0.9991 \|
	\| 0.9074 \| 9500 \| 0.0968 \| 0.0813 \| 0.9990 \|
	\| 0.9169 \| 9600 \| 0.1108 \| 0.0814 \| 0.9991 \|
	\| 0.9265 \| 9700 \| 0.1058 \| 0.0806 \| 0.9991 \|
	\| 0.9360 \| 9800 \| 0.0871 \| 0.0800 \| 0.9990 \|
	\| 0.9456 \| 9900 \| 0.1079 \| 0.0797 \| 0.9991 \|
	\| 0.9551 \| 10000 \| 0.1064 \| 0.0794 \| 0.9991 \|
	\| 0.9647 \| 10100 \| 0.1095 \| 0.0792 \| 0.9991 \|
	\| 0.9742 \| 10200 \| 0.0858 \| 0.0791 \| 0.9991 \|
	\| 0.9838 \| 10300 \| 0.0997 \| 0.0791 \| 0.9991 \|
	\| 0.9933 \| 10400 \| 0.0888 \| 0.0791 \| 0.9991 \|

	</details>

	### Framework Versions
	- Python: 3.12.7
	- Sentence Transformers: 4.1.0
	- Transformers: 4.51.3
	- PyTorch: 2.4.1+cu124
	- Accelerate: 1.6.0
	- Datasets: 3.5.0
	- 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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