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	---
	base_model: Snowflake/snowflake-arctic-embed-xs
	language:
	- en
	library_name: sentence-transformers
	license: apache-2.0
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:416298
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: The radial profiles using frank for the seven targets can be seen
	in Figure 6.
	sentences:
	- At longer radio wavelengths, we selected the newest observations of the appropriate
	resolution from the VLA archive.
	- The radial profiles using frank for the seven targets can be seen in Figure 6.
	- For further information on observation and data calibration, refer to Hunt et al.
	(2021).
	- source_sentence: They are extragalactic scaled up versions of galactic Ultra Compact
	(UC) H ii regions, which are typically excited by a single massive star and are
	≲less-than-or-similar-to\lesssim 0.1 pc in size (Wood & Churchwell, 1989).
	sentences:
	- They are extragalactic scaled up versions of galactic Ultra Compact (UC) H ii
	regions, which are typically excited by a single massive star and are ≲less-than-or-similar-to\lesssim
	0.1 pc in size (Wood & Churchwell, 1989).
	- The LMT is a project operated by the Instituto Nacional de Astrófisica, Óptica,
	y Electrónica (Mexico) and the University of Massachusetts at Amherst (USA).
	- We measure the detection confidence in the resolved image as the ratio between
	the local mean posterior and the local posterior standard deviation of the estimated
	circular polarization, evaluated based on 1000 images drawn from the posterior
	distribution.
	- source_sentence: The flux density calibrator was 3C286, and the complex gain calibrator
	was J0836-2016.
	sentences:
	- The flux density calibrator was 3C286, and the complex gain calibrator was J0836-2016.
	- While rcsubscript𝑟cr_{\rm c} has a clear dependence on Dmaxsubscript𝐷maxD_{\rm
	max}, xMMSNsubscript𝑥MMSNx_{\rm MMSN} and tagesubscript𝑡aget_{\rm age}, ΣcsubscriptΣc\Sigma_{\rm
	c} only has weak dependence on Dmaxsubscript𝐷maxD_{\rm max}, and so is mostly
	sensitive to the scaling of the total initial planetesimal mass, xMMSNsubscript𝑥MMSNx_{\rm
	MMSN} and tagesubscript𝑡aget_{\rm age}.
	- 20 is valid only at r=rc𝑟subscript𝑟cr=r_{\rm c}, it has been shown that the surface
	density of dust at r>rc𝑟subscript𝑟cr>r_{\rm c} is expected to be flat for a primordial
	surface density exponent (−α𝛼-\alpha) of -3/2, or more generally proportional
	to r−0.6α+0.9superscript𝑟0.6𝛼0.9r^{-0.6\alpha+0.9} (Schüppler et al., 2016; Marino
	et al., 2017b; Geiler & Krivov, 2017).
	- source_sentence: We would like to thank A. Deller and W. Brisken for EHT-specific
	support with the use of DiFX.
	sentences:
	- Ice has one of the weakest strengths, and thus if we had assumed stronger solids
	the derived values of Dmaxsubscript𝐷D_{\max} and xMMSNsubscript𝑥MMSNx_{\rm MMSN}
	would be lower.
	- We would like to thank A. Deller and W. Brisken for EHT-specific support with
	the use of DiFX.
	- The wsmoothsubscript𝑤smoothw_{\rm smooth} chosen parameter ranged from 10−2superscript10210^{-2}
	to 10−4superscript10410^{-4} depending on the disc.
	- source_sentence: New higher resolution images and our parametric modelling confirmed
	this finding.
	sentences:
	- New higher resolution images and our parametric modelling confirmed this finding.
	- With the 3-bit correlator configuration, we obtained a total bandwidth of ∼similar-to\sim8 GHz
	across Ka-band.
	- Pan & Schlichting, 2012) and thus could slightly affect the surface density slope.
	---

	# interstellar-ice-crystal-xs

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs). 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.
	This was a proof-of-method model: it was created to show the applicability of some techniques to a certain dataset. It is not, however, really an improvement on the base model, and I advise against using in production.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) <!-- at revision 742da4f66e1823b5b4dbe6c320a1375a1fd85f9e -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset: scraped astronomy papers at the NLP for Space Science workshop.
	- 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({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, '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("SimoneAstarita/interstellar-ice-crystal-xs")
	# Run inference
	sentences = [
	'New higher resolution images and our parametric modelling confirmed this finding.',
	'New higher resolution images and our parametric modelling confirmed this finding.',
	'Pan & Schlichting, 2012) and thus could slightly affect the surface density slope.',
	]
	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.
	-->

	<!--
	## 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

	The dataset is made of scrapes papers in astronomy, including abstract, introduction and conclusions. They are divided into sentences using nklt. We then duplicate them and train using the same senrence for positive and anchor. We are using SimSCE.

	#### Unnamed Dataset


	* Size: 416,298 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 42.81 tokens</li><li>max: 512 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 42.81 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope</code> \| <code>Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope</code> \|
	\| <code>The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).</code> \| <code>The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).</code> \|
	\| <code>The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).</code> \| <code>The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).</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

	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: 3
	- `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`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `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
	- `eval_use_gather_object`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:-----:\|:-------------:\|
	\| 0.0077 \| 100 \| 0.0025 \|
	\| 0.0154 \| 200 \| 0.0032 \|
	\| 0.0231 \| 300 \| 0.0026 \|
	\| 0.0307 \| 400 \| 0.0026 \|
	\| 0.0384 \| 500 \| 0.0041 \|
	\| 0.0461 \| 600 \| 0.0014 \|
	\| 0.0538 \| 700 \| 0.0019 \|
	\| 0.0615 \| 800 \| 0.0015 \|
	\| 0.0692 \| 900 \| 0.001 \|
	\| 0.0769 \| 1000 \| 0.0005 \|
	\| 0.0846 \| 1100 \| 0.0004 \|
	\| 0.0922 \| 1200 \| 0.0013 \|
	\| 0.0999 \| 1300 \| 0.0013 \|
	\| 0.1076 \| 1400 \| 0.0027 \|
	\| 0.1153 \| 1500 \| 0.0018 \|
	\| 0.1230 \| 1600 \| 0.001 \|
	\| 0.1307 \| 1700 \| 0.0014 \|
	\| 0.1384 \| 1800 \| 0.0012 \|
	\| 0.1460 \| 1900 \| 0.0041 \|
	\| 0.1537 \| 2000 \| 0.0009 \|
	\| 0.1614 \| 2100 \| 0.0005 \|
	\| 0.1691 \| 2200 \| 0.0011 \|
	\| 0.1768 \| 2300 \| 0.001 \|
	\| 0.1845 \| 2400 \| 0.0004 \|
	\| 0.1922 \| 2500 \| 0.0011 \|
	\| 0.1998 \| 2600 \| 0.0044 \|
	\| 0.2075 \| 2700 \| 0.0004 \|
	\| 0.2152 \| 2800 \| 0.0022 \|
	\| 0.2229 \| 2900 \| 0.0007 \|
	\| 0.2306 \| 3000 \| 0.0006 \|
	\| 0.2383 \| 3100 \| 0.0002 \|
	\| 0.2460 \| 3200 \| 0.0006 \|
	\| 0.2537 \| 3300 \| 0.0004 \|
	\| 0.2613 \| 3400 \| 0.0013 \|
	\| 0.2690 \| 3500 \| 0.0006 \|
	\| 0.2767 \| 3600 \| 0.0005 \|
	\| 0.2844 \| 3700 \| 0.0018 \|
	\| 0.2921 \| 3800 \| 0.0023 \|
	\| 0.2998 \| 3900 \| 0.0011 \|
	\| 0.3075 \| 4000 \| 0.0007 \|
	\| 0.3151 \| 4100 \| 0.0008 \|
	\| 0.3228 \| 4200 \| 0.0013 \|
	\| 0.3305 \| 4300 \| 0.0012 \|
	\| 0.3382 \| 4400 \| 0.001 \|
	\| 0.3459 \| 4500 \| 0.0016 \|
	\| 0.3536 \| 4600 \| 0.0025 \|
	\| 0.3613 \| 4700 \| 0.0015 \|
	\| 0.3689 \| 4800 \| 0.0018 \|
	\| 0.3766 \| 4900 \| 0.0019 \|
	\| 0.3843 \| 5000 \| 0.0021 \|
	\| 0.3920 \| 5100 \| 0.0018 \|
	\| 0.3997 \| 5200 \| 0.0004 \|
	\| 0.4074 \| 5300 \| 0.0006 \|
	\| 0.4151 \| 5400 \| 0.0007 \|
	\| 0.4228 \| 5500 \| 0.0009 \|
	\| 0.4304 \| 5600 \| 0.0004 \|
	\| 0.4381 \| 5700 \| 0.0003 \|
	\| 0.4458 \| 5800 \| 0.0007 \|
	\| 0.4535 \| 5900 \| 0.0013 \|
	\| 0.4612 \| 6000 \| 0.0007 \|
	\| 0.4689 \| 6100 \| 0.0005 \|
	\| 0.4766 \| 6200 \| 0.001 \|
	\| 0.4842 \| 6300 \| 0.0027 \|
	\| 0.4919 \| 6400 \| 0.0018 \|
	\| 0.4996 \| 6500 \| 0.0006 \|
	\| 0.5073 \| 6600 \| 0.0008 \|
	\| 0.5150 \| 6700 \| 0.0006 \|
	\| 0.5227 \| 6800 \| 0.0007 \|
	\| 0.5304 \| 6900 \| 0.001 \|
	\| 0.5380 \| 7000 \| 0.0007 \|
	\| 0.5457 \| 7100 \| 0.0005 \|
	\| 0.5534 \| 7200 \| 0.0012 \|
	\| 0.5611 \| 7300 \| 0.0012 \|
	\| 0.5688 \| 7400 \| 0.0011 \|
	\| 0.5765 \| 7500 \| 0.0005 \|
	\| 0.5842 \| 7600 \| 0.0013 \|
	\| 0.5919 \| 7700 \| 0.0012 \|
	\| 0.5995 \| 7800 \| 0.0007 \|
	\| 0.6072 \| 7900 \| 0.0012 \|
	\| 0.6149 \| 8000 \| 0.0012 \|
	\| 0.6226 \| 8100 \| 0.0003 \|
	\| 0.6303 \| 8200 \| 0.0003 \|
	\| 0.6380 \| 8300 \| 0.0003 \|
	\| 0.6457 \| 8400 \| 0.002 \|
	\| 0.6533 \| 8500 \| 0.0003 \|
	\| 0.6610 \| 8600 \| 0.0016 \|
	\| 0.6687 \| 8700 \| 0.0003 \|
	\| 0.6764 \| 8800 \| 0.0002 \|
	\| 0.6841 \| 8900 \| 0.0006 \|
	\| 0.6918 \| 9000 \| 0.0005 \|
	\| 0.6995 \| 9100 \| 0.0017 \|
	\| 0.7071 \| 9200 \| 0.0037 \|
	\| 0.7148 \| 9300 \| 0.0005 \|
	\| 0.7225 \| 9400 \| 0.0006 \|
	\| 0.7302 \| 9500 \| 0.0004 \|
	\| 0.7379 \| 9600 \| 0.0002 \|
	\| 0.7456 \| 9700 \| 0.0008 \|
	\| 0.7533 \| 9800 \| 0.0005 \|
	\| 0.7610 \| 9900 \| 0.0006 \|
	\| 0.7686 \| 10000 \| 0.0004 \|
	\| 0.7763 \| 10100 \| 0.0004 \|
	\| 0.7840 \| 10200 \| 0.0006 \|
	\| 0.7917 \| 10300 \| 0.0019 \|
	\| 0.7994 \| 10400 \| 0.0007 \|
	\| 0.8071 \| 10500 \| 0.0003 \|
	\| 0.8148 \| 10600 \| 0.0003 \|
	\| 0.8224 \| 10700 \| 0.0005 \|
	\| 0.8301 \| 10800 \| 0.0009 \|
	\| 0.8378 \| 10900 \| 0.0006 \|
	\| 0.8455 \| 11000 \| 0.002 \|
	\| 0.8532 \| 11100 \| 0.0018 \|
	\| 0.8609 \| 11200 \| 0.0009 \|
	\| 0.8686 \| 11300 \| 0.0004 \|
	\| 0.8762 \| 11400 \| 0.0005 \|
	\| 0.8839 \| 11500 \| 0.0008 \|
	\| 0.8916 \| 11600 \| 0.0003 \|
	\| 0.8993 \| 11700 \| 0.0002 \|
	\| 0.9070 \| 11800 \| 0.0004 \|
	\| 0.9147 \| 11900 \| 0.0007 \|
	\| 0.9224 \| 12000 \| 0.0009 \|
	\| 0.9301 \| 12100 \| 0.0007 \|
	\| 0.9377 \| 12200 \| 0.0007 \|
	\| 0.9454 \| 12300 \| 0.0009 \|
	\| 0.9531 \| 12400 \| 0.0007 \|
	\| 0.9608 \| 12500 \| 0.0009 \|
	\| 0.9685 \| 12600 \| 0.0004 \|
	\| 0.9762 \| 12700 \| 0.0002 \|
	\| 0.9839 \| 12800 \| 0.0003 \|
	\| 0.9915 \| 12900 \| 0.0002 \|
	\| 0.9992 \| 13000 \| 0.0002 \|
	\| 1.0069 \| 13100 \| 0.0006 \|
	\| 1.0146 \| 13200 \| 0.0007 \|
	\| 1.0223 \| 13300 \| 0.0007 \|
	\| 1.0300 \| 13400 \| 0.0005 \|
	\| 1.0377 \| 13500 \| 0.0008 \|
	\| 1.0453 \| 13600 \| 0.0016 \|
	\| 1.0530 \| 13700 \| 0.0007 \|
	\| 1.0607 \| 13800 \| 0.0013 \|
	\| 1.0684 \| 13900 \| 0.0005 \|
	\| 1.0761 \| 14000 \| 0.0002 \|
	\| 1.0838 \| 14100 \| 0.0001 \|
	\| 1.0915 \| 14200 \| 0.0003 \|
	\| 1.0992 \| 14300 \| 0.0003 \|
	\| 1.1068 \| 14400 \| 0.0006 \|
	\| 1.1145 \| 14500 \| 0.0002 \|
	\| 1.1222 \| 14600 \| 0.0003 \|
	\| 1.1299 \| 14700 \| 0.0002 \|
	\| 1.1376 \| 14800 \| 0.0006 \|
	\| 1.1453 \| 14900 \| 0.0011 \|
	\| 1.1530 \| 15000 \| 0.0004 \|
	\| 1.1606 \| 15100 \| 0.0001 \|
	\| 1.1683 \| 15200 \| 0.0003 \|
	\| 1.1760 \| 15300 \| 0.0001 \|
	\| 1.1837 \| 15400 \| 0.0002 \|
	\| 1.1914 \| 15500 \| 0.0001 \|
	\| 1.1991 \| 15600 \| 0.003 \|
	\| 1.2068 \| 15700 \| 0.0001 \|
	\| 1.2145 \| 15800 \| 0.0002 \|
	\| 1.2221 \| 15900 \| 0.0005 \|
	\| 1.2298 \| 16000 \| 0.0004 \|
	\| 1.2375 \| 16100 \| 0.0001 \|
	\| 1.2452 \| 16200 \| 0.0003 \|
	\| 1.2529 \| 16300 \| 0.0003 \|
	\| 1.2606 \| 16400 \| 0.0008 \|
	\| 1.2683 \| 16500 \| 0.0004 \|
	\| 1.2759 \| 16600 \| 0.0001 \|
	\| 1.2836 \| 16700 \| 0.0002 \|
	\| 1.2913 \| 16800 \| 0.0011 \|
	\| 1.2990 \| 16900 \| 0.0001 \|
	\| 1.3067 \| 17000 \| 0.0001 \|
	\| 1.3144 \| 17100 \| 0.0002 \|
	\| 1.3221 \| 17200 \| 0.0005 \|
	\| 1.3297 \| 17300 \| 0.0012 \|
	\| 1.3374 \| 17400 \| 0.0003 \|
	\| 1.3451 \| 17500 \| 0.0002 \|
	\| 1.3528 \| 17600 \| 0.0009 \|
	\| 1.3605 \| 17700 \| 0.0003 \|
	\| 1.3682 \| 17800 \| 0.0005 \|
	\| 1.3759 \| 17900 \| 0.0008 \|
	\| 1.3836 \| 18000 \| 0.0005 \|
	\| 1.3912 \| 18100 \| 0.0007 \|
	\| 1.3989 \| 18200 \| 0.0002 \|
	\| 1.4066 \| 18300 \| 0.0003 \|
	\| 1.4143 \| 18400 \| 0.0002 \|
	\| 1.4220 \| 18500 \| 0.0001 \|
	\| 1.4297 \| 18600 \| 0.0001 \|
	\| 1.4374 \| 18700 \| 0.0001 \|
	\| 1.4450 \| 18800 \| 0.0005 \|
	\| 1.4527 \| 18900 \| 0.0002 \|
	\| 1.4604 \| 19000 \| 0.0001 \|
	\| 1.4681 \| 19100 \| 0.0002 \|
	\| 1.4758 \| 19200 \| 0.0006 \|
	\| 1.4835 \| 19300 \| 0.0015 \|
	\| 1.4912 \| 19400 \| 0.0012 \|
	\| 1.4988 \| 19500 \| 0.0003 \|
	\| 1.5065 \| 19600 \| 0.0005 \|
	\| 1.5142 \| 19700 \| 0.0001 \|
	\| 1.5219 \| 19800 \| 0.0002 \|
	\| 1.5296 \| 19900 \| 0.0009 \|
	\| 1.5373 \| 20000 \| 0.0002 \|
	\| 1.5450 \| 20100 \| 0.0001 \|
	\| 1.5527 \| 20200 \| 0.0003 \|
	\| 1.5603 \| 20300 \| 0.0006 \|
	\| 1.5680 \| 20400 \| 0.0002 \|
	\| 1.5757 \| 20500 \| 0.0004 \|
	\| 1.5834 \| 20600 \| 0.0006 \|
	\| 1.5911 \| 20700 \| 0.0004 \|
	\| 1.5988 \| 20800 \| 0.0002 \|
	\| 1.6065 \| 20900 \| 0.0006 \|
	\| 1.6141 \| 21000 \| 0.0006 \|
	\| 1.6218 \| 21100 \| 0.0001 \|
	\| 1.6295 \| 21200 \| 0.0001 \|
	\| 1.6372 \| 21300 \| 0.0001 \|
	\| 1.6449 \| 21400 \| 0.0008 \|
	\| 1.6526 \| 21500 \| 0.0001 \|
	\| 1.6603 \| 21600 \| 0.0005 \|
	\| 1.6679 \| 21700 \| 0.0001 \|
	\| 1.6756 \| 21800 \| 0.0001 \|
	\| 1.6833 \| 21900 \| 0.0001 \|
	\| 1.6910 \| 22000 \| 0.0001 \|
	\| 1.6987 \| 22100 \| 0.0008 \|
	\| 1.7064 \| 22200 \| 0.0014 \|
	\| 1.7141 \| 22300 \| 0.0002 \|
	\| 1.7218 \| 22400 \| 0.0007 \|
	\| 1.7294 \| 22500 \| 0.0001 \|
	\| 1.7371 \| 22600 \| 0.0001 \|
	\| 1.7448 \| 22700 \| 0.0001 \|
	\| 1.7525 \| 22800 \| 0.0002 \|
	\| 1.7602 \| 22900 \| 0.0002 \|
	\| 1.7679 \| 23000 \| 0.0001 \|
	\| 1.7756 \| 23100 \| 0.0001 \|
	\| 1.7832 \| 23200 \| 0.0005 \|
	\| 1.7909 \| 23300 \| 0.0004 \|
	\| 1.7986 \| 23400 \| 0.0002 \|
	\| 1.8063 \| 23500 \| 0.0001 \|
	\| 1.8140 \| 23600 \| 0.0001 \|
	\| 1.8217 \| 23700 \| 0.0001 \|
	\| 1.8294 \| 23800 \| 0.0004 \|
	\| 1.8370 \| 23900 \| 0.0002 \|
	\| 1.8447 \| 24000 \| 0.0002 \|
	\| 1.8524 \| 24100 \| 0.0013 \|
	\| 1.8601 \| 24200 \| 0.0004 \|
	\| 1.8678 \| 24300 \| 0.0002 \|
	\| 1.8755 \| 24400 \| 0.0002 \|
	\| 1.8832 \| 24500 \| 0.0001 \|
	\| 1.8909 \| 24600 \| 0.0001 \|
	\| 1.8985 \| 24700 \| 0.0001 \|
	\| 1.9062 \| 24800 \| 0.0002 \|
	\| 1.9139 \| 24900 \| 0.0005 \|
	\| 1.9216 \| 25000 \| 0.0001 \|
	\| 1.9293 \| 25100 \| 0.0001 \|
	\| 1.9370 \| 25200 \| 0.0002 \|
	\| 1.9447 \| 25300 \| 0.0002 \|
	\| 1.9523 \| 25400 \| 0.0006 \|
	\| 1.9600 \| 25500 \| 0.0004 \|
	\| 1.9677 \| 25600 \| 0.0002 \|
	\| 1.9754 \| 25700 \| 0.0001 \|
	\| 1.9831 \| 25800 \| 0.0001 \|
	\| 1.9908 \| 25900 \| 0.0001 \|
	\| 1.9985 \| 26000 \| 0.0001 \|
	\| 2.0061 \| 26100 \| 0.0002 \|
	\| 2.0138 \| 26200 \| 0.0007 \|
	\| 2.0215 \| 26300 \| 0.0003 \|
	\| 2.0292 \| 26400 \| 0.0001 \|
	\| 2.0369 \| 26500 \| 0.0011 \|
	\| 2.0446 \| 26600 \| 0.0002 \|
	\| 2.0523 \| 26700 \| 0.0001 \|
	\| 2.0600 \| 26800 \| 0.0002 \|
	\| 2.0676 \| 26900 \| 0.0004 \|
	\| 2.0753 \| 27000 \| 0.0001 \|
	\| 2.0830 \| 27100 \| 0.0001 \|
	\| 2.0907 \| 27200 \| 0.0001 \|
	\| 2.0984 \| 27300 \| 0.0002 \|
	\| 2.1061 \| 27400 \| 0.0001 \|
	\| 2.1138 \| 27500 \| 0.0001 \|
	\| 2.1214 \| 27600 \| 0.0001 \|
	\| 2.1291 \| 27700 \| 0.0001 \|
	\| 2.1368 \| 27800 \| 0.0003 \|
	\| 2.1445 \| 27900 \| 0.0012 \|
	\| 2.1522 \| 28000 \| 0.0001 \|
	\| 2.1599 \| 28100 \| 0.0001 \|
	\| 2.1676 \| 28200 \| 0.0001 \|
	\| 2.1752 \| 28300 \| 0.0001 \|
	\| 2.1829 \| 28400 \| 0.0001 \|
	\| 2.1906 \| 28500 \| 0.0001 \|
	\| 2.1983 \| 28600 \| 0.0014 \|
	\| 2.2060 \| 28700 \| 0.0001 \|
	\| 2.2137 \| 28800 \| 0.0001 \|
	\| 2.2214 \| 28900 \| 0.0002 \|
	\| 2.2291 \| 29000 \| 0.0 \|
	\| 2.2367 \| 29100 \| 0.0001 \|
	\| 2.2444 \| 29200 \| 0.0001 \|
	\| 2.2521 \| 29300 \| 0.0001 \|
	\| 2.2598 \| 29400 \| 0.0001 \|
	\| 2.2675 \| 29500 \| 0.0001 \|
	\| 2.2752 \| 29600 \| 0.0001 \|
	\| 2.2829 \| 29700 \| 0.0001 \|
	\| 2.2905 \| 29800 \| 0.0001 \|
	\| 2.2982 \| 29900 \| 0.0001 \|
	\| 2.3059 \| 30000 \| 0.0001 \|
	\| 2.3136 \| 30100 \| 0.0001 \|
	\| 2.3213 \| 30200 \| 0.0002 \|
	\| 2.3290 \| 30300 \| 0.0011 \|
	\| 2.3367 \| 30400 \| 0.0001 \|
	\| 2.3444 \| 30500 \| 0.0001 \|
	\| 2.3520 \| 30600 \| 0.0005 \|
	\| 2.3597 \| 30700 \| 0.0001 \|
	\| 2.3674 \| 30800 \| 0.0001 \|
	\| 2.3751 \| 30900 \| 0.0006 \|
	\| 2.3828 \| 31000 \| 0.0001 \|
	\| 2.3905 \| 31100 \| 0.0001 \|
	\| 2.3982 \| 31200 \| 0.0002 \|
	\| 2.4058 \| 31300 \| 0.0001 \|
	\| 2.4135 \| 31400 \| 0.0001 \|
	\| 2.4212 \| 31500 \| 0.0001 \|
	\| 2.4289 \| 31600 \| 0.0001 \|
	\| 2.4366 \| 31700 \| 0.0001 \|
	\| 2.4443 \| 31800 \| 0.0004 \|
	\| 2.4520 \| 31900 \| 0.0001 \|
	\| 2.4596 \| 32000 \| 0.0001 \|
	\| 2.4673 \| 32100 \| 0.0002 \|
	\| 2.4750 \| 32200 \| 0.0002 \|
	\| 2.4827 \| 32300 \| 0.0004 \|
	\| 2.4904 \| 32400 \| 0.0008 \|
	\| 2.4981 \| 32500 \| 0.0001 \|
	\| 2.5058 \| 32600 \| 0.0001 \|
	\| 2.5135 \| 32700 \| 0.0001 \|
	\| 2.5211 \| 32800 \| 0.0001 \|
	\| 2.5288 \| 32900 \| 0.0006 \|
	\| 2.5365 \| 33000 \| 0.0001 \|
	\| 2.5442 \| 33100 \| 0.0001 \|
	\| 2.5519 \| 33200 \| 0.0002 \|
	\| 2.5596 \| 33300 \| 0.0001 \|
	\| 2.5673 \| 33400 \| 0.0002 \|
	\| 2.5749 \| 33500 \| 0.0001 \|
	\| 2.5826 \| 33600 \| 0.0001 \|
	\| 2.5903 \| 33700 \| 0.0001 \|
	\| 2.5980 \| 33800 \| 0.0001 \|
	\| 2.6057 \| 33900 \| 0.0001 \|
	\| 2.6134 \| 34000 \| 0.0007 \|
	\| 2.6211 \| 34100 \| 0.0 \|
	\| 2.6287 \| 34200 \| 0.0001 \|
	\| 2.6364 \| 34300 \| 0.0001 \|
	\| 2.6441 \| 34400 \| 0.0006 \|
	\| 2.6518 \| 34500 \| 0.0001 \|
	\| 2.6595 \| 34600 \| 0.0001 \|
	\| 2.6672 \| 34700 \| 0.0001 \|
	\| 2.6749 \| 34800 \| 0.0 \|
	\| 2.6826 \| 34900 \| 0.0001 \|
	\| 2.6902 \| 35000 \| 0.0001 \|
	\| 2.6979 \| 35100 \| 0.0005 \|
	\| 2.7056 \| 35200 \| 0.0006 \|
	\| 2.7133 \| 35300 \| 0.0001 \|
	\| 2.7210 \| 35400 \| 0.0005 \|
	\| 2.7287 \| 35500 \| 0.0001 \|
	\| 2.7364 \| 35600 \| 0.0001 \|
	\| 2.7440 \| 35700 \| 0.0001 \|
	\| 2.7517 \| 35800 \| 0.0001 \|
	\| 2.7594 \| 35900 \| 0.0001 \|
	\| 2.7671 \| 36000 \| 0.0001 \|
	\| 2.7748 \| 36100 \| 0.0001 \|
	\| 2.7825 \| 36200 \| 0.0005 \|
	\| 2.7902 \| 36300 \| 0.0001 \|
	\| 2.7978 \| 36400 \| 0.0001 \|
	\| 2.8055 \| 36500 \| 0.0001 \|
	\| 2.8132 \| 36600 \| 0.0001 \|
	\| 2.8209 \| 36700 \| 0.0001 \|
	\| 2.8286 \| 36800 \| 0.0001 \|
	\| 2.8363 \| 36900 \| 0.0001 \|
	\| 2.8440 \| 37000 \| 0.0001 \|
	\| 2.8517 \| 37100 \| 0.0013 \|
	\| 2.8593 \| 37200 \| 0.0001 \|
	\| 2.8670 \| 37300 \| 0.0001 \|
	\| 2.8747 \| 37400 \| 0.0001 \|
	\| 2.8824 \| 37500 \| 0.0001 \|
	\| 2.8901 \| 37600 \| 0.0001 \|
	\| 2.8978 \| 37700 \| 0.0001 \|
	\| 2.9055 \| 37800 \| 0.0001 \|
	\| 2.9131 \| 37900 \| 0.0002 \|
	\| 2.9208 \| 38000 \| 0.0001 \|
	\| 2.9285 \| 38100 \| 0.0001 \|
	\| 2.9362 \| 38200 \| 0.0001 \|
	\| 2.9439 \| 38300 \| 0.0001 \|
	\| 2.9516 \| 38400 \| 0.0004 \|
	\| 2.9593 \| 38500 \| 0.0001 \|
	\| 2.9669 \| 38600 \| 0.0001 \|
	\| 2.9746 \| 38700 \| 0.0001 \|
	\| 2.9823 \| 38800 \| 0.0001 \|
	\| 2.9900 \| 38900 \| 0.0001 \|
	\| 2.9977 \| 39000 \| 0.0001 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.1.0
	- Transformers: 4.44.2
	- PyTorch: 2.4.0+cu121
	- Accelerate: 0.34.2
	- Datasets: 3.0.0
	- Tokenizers: 0.19.1

	## 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}
	}
	```

	#Add SimSCE reference

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