ModernBERT-large-DPR-msmarco / README.md

Add new SentenceTransformer model

9ecc13e verified 3 days ago

30.8 kB

	---
	language:
	- en
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:11662655
	- loss:CachedMultipleNegativesRankingLoss
	base_model: answerdotai/ModernBERT-large
	widget:
	- source_sentence: where is henderson mn
	sentences:
	- Confidence votes 1.7K. Assuming we're talking about the `usual' 12 volt car battery'
	the resting voltage should be around 11 to 11.5 volts. Under charge it's as high
	as 15 volts as supplied from the alternator,and most cars won't start if the voltage
	is under 10.5 to 11.5 volts. The term `12 volt battery' is what's referred to
	as, `nominal' or `in name only' as a general reference and not meant to be an
	accurate description.
	- Henderson is a very small town of 1,000 people on the west bank of the Minnesota
	River just south of the Minneapolis and Saint Paul metro area.
	- Henderson, officially the City of Henderson, is an affluent city in Clark County,
	Nevada, United States, about 16 miles southeast of Las Vegas. It is the second-largest
	city in Nevada, after Las Vegas, with an estimated population of 292,969 in 2016.[2]
	The city is part of the Las Vegas metropolitan area, which spans the entire Las
	Vegas Valley. Henderson occupies the southeastern end of the valley, at an elevation
	of approximately 1,330 feet (410 m).
	- source_sentence: polytomy definition
	sentences:
	- Polytomy definition, the act or process of dividing into more than three parts.
	See more.
	- 'The name Loyalty has the following meaning: One who is faithful, loyal. It is
	a male name, suitable for baby boys. Origins. The name Loyalty is very likely
	a(n) English variant of the name Loyal. See other suggested English boy baby names.
	You might also like to see the other variants of the name Loyal.'
	- "Polysemy (/pÉ\x99Ë\x88lÉªsÉªmi/ or /Ë\x88pÉ\x92lÉªsiË\x90mi/; from Greek: Ï\x80\
	Î¿Î»Ï\N-, poly-, many and Ï\x83á¿\x86Î¼Î±, sÃªma, sign) is the capacity for a\
	\ sign (such as a word, phrase, or symbol) to have multiple meanings (that is,\
	\ multiple semes or sememes and thus multiple senses), usually related by contiguity\
	\ of meaning within a semantic field."
	- source_sentence: age group for juvenile arthritis
	sentences:
	- "Different Types of Juvenile Rheumatoid Arthritis. There are three kinds. Each\
	\ type is based on the number of joints involved, the symptoms, and certain antibodies\
	\ that may be in the blood. Four or fewer joints are involved. Doctors call this\
	\ pauciarticular JRA. Itâ\x80\x99s the most common form. About half of all children\
	\ with juvenile rheumatoid arthritis have this type. It usually affects large\
	\ joints like the knees. Girls under age 8 are most likely to get it."
	- Juvenile rheumatoid arthritis (JRA), often referred to by doctors today as juvenile
	idiopathic arthritis (JIA), is a type of arthritis that causes joint inflammation
	and stiffness for more than six weeks in a child aged 16 or younger. It affects
	approximately 50,000 children in the United States.
	- A depressant, or central depressant, is a drug that lowers neurotransmission levels,
	which is to depress or reduce arousal or stimulation, in various areas of the
	brain.Depressants are also occasionally referred to as downers as they lower the
	level of arousal when taken.istilled (concentrated) alcoholic beverages, often
	called hard liquor , roughly eight times more alcoholic than beer. An alcoholic
	beverage is a drink that contains ethanol, an anesthetic that has been used as
	a psychoactive drug for several millennia. Ethanol is the oldest recreational
	drug still used by humans.
	- source_sentence: what is besivance and durezol used for
	sentences:
	- Besivance is antibiotic eye drops, Prolensa is antiinflammatory eye drop and Durezol
	is steroid eye drop. Besivance and Prolensa are need to be taken from 1-3 days
	prior to surgery as a prophylaxis to prevent postoperative infection and inflammation
	respectively. These eye drops can be administered after at least a gap of 5 minutes.
	They are needed to be administered at least 4 times per day.
	- .23 Acres Comfort, Kendall County, Texas. $399,500. This could be the most well
	known building in Comfort with excellent all around visibility. Constructed in
	the early 1930's and initially used as a bar it ...
	- Duloxetine is used to treat major depressive disorder and general anxiety disorder.
	Duloxetine is also used to treat fibromyalgia (a chronic pain disorder), or chronic
	muscle or joint pain (such as low back pain and osteoarthritis pain). Duloxetine
	is also used to treat pain caused by nerve damage in people with diabetes (diabetic
	neuropathy).
	- source_sentence: do bond funds pay dividends
	sentences:
	- If a cavity is causing the toothache, your dentist will fill the cavity or possibly
	extract the tooth, if necessary. A root canal might be needed if the cause of
	the toothache is determined to be an infection of the tooth's nerve. Bacteria
	that have worked their way into the inner aspects of the tooth cause such an infection.
	An antibiotic may be prescribed if there is fever or swelling of the jaw.
	- "You would have $71,200 paying out $1,687 in annual dividends. That is about $4.62\
	\ for working up in the morning. Interestingly enough, that 2.37% yield is at\
	\ a low point because The Wellington Fund is a â\x80\x9Cbalanced fundâ\x80\x9D\
	\ meaning that it holds a combination of stocks and bonds."
	- A bond fund or debt fund is a fund that invests in bonds, or other debt securities.
	Bond funds can be contrasted with stock funds and money funds. Bond funds typically
	pay periodic dividends that include interest payments on the fund's underlying
	securities plus periodic realized capital appreciation. Bond funds typically pay
	higher dividends than CDs and money market accounts. Most bond funds pay out dividends
	more frequently than individual bonds.
	datasets:
	- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	model-index:
	- name: SentenceTransformer based on answerdotai/ModernBERT-large
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: msmarco co condenser dev
	type: msmarco-co-condenser-dev
	metrics:
	- type: cosine_accuracy
	value: 0.991
	name: Cosine Accuracy
	---

	# SentenceTransformer based on answerdotai/ModernBERT-large

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on the [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) 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: [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision f87846cf8be76fceb18718f0245d18c8e6571215 -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
	- 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: ModernBertModel
	(1): Pooling({'word_embedding_dimension': 1024, '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})
	)
	```

	## 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("makiart/ModernBERT-large-DPR-8e-05")
	# Run inference
	sentences = [
	'do bond funds pay dividends',
	"A bond fund or debt fund is a fund that invests in bonds, or other debt securities. Bond funds can be contrasted with stock funds and money funds. Bond funds typically pay periodic dividends that include interest payments on the fund's underlying securities plus periodic realized capital appreciation. Bond funds typically pay higher dividends than CDs and money market accounts. Most bond funds pay out dividends more frequently than individual bonds.",
	'You would have $71,200 paying out $1,687 in annual dividends. That is about $4.62 for working up in the morning. Interestingly enough, that 2.37% yield is at a low point because The Wellington Fund is a â\x80\x9cbalanced fundâ\x80\x9d meaning that it holds a combination of stocks and bonds.',
	]
	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]
	```

	<!--
	### 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: `msmarco-co-condenser-dev`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| cosine_accuracy \| 0.991 \|

	<!--
	## 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

	#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1

	* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
	* Size: 11,662,655 training samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:---------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 9.26 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 79.14 tokens</li><li>max: 222 tokens</li></ul> \| <ul><li>min: 24 tokens</li><li>mean: 80.09 tokens</li><li>max: 436 tokens</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:---------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>what is the meaning of menu planning</code> \| <code>Menu planning is the selection of a menu for an event. Such as picking out the dinner for your wedding or even a meal at a Birthday Party. Menu planning is when you are preparing a calendar of meals and you have to sit down and decide what meat and veggies you want to serve on each certain day.</code> \| <code>Menu Costs. In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general.</code> \|
	\| <code>how old is brett butler</code> \| <code>Brett Butler is 59 years old. To be more precise (and nerdy), the current age as of right now is 21564 days or (even more geeky) 517536 hours. That's a lot of hours!</code> \| <code>Passed in: St. John's, Newfoundland and Labrador, Canada. Passed on: 16/07/2016. Published in the St. John's Telegram. Passed away suddenly at the Health Sciences Centre surrounded by his loving family, on July 16, 2016 Robert (Bobby) Joseph Butler, age 52 years. Predeceased by his special aunt Geri Murrin and uncle Mike Mchugh; grandparents Joe and Margaret Murrin and Jack and Theresa Butler.</code> \|
	\| <code>when was the last navajo treaty sign?</code> \| <code>In Executive Session, Senate of the United States, July 25, 1868. Resolved, (two-thirds of the senators present concurring,) That the Senate advise and consent to the ratification of the treaty between the United States and the Navajo Indians, concluded at Fort Sumner, New Mexico, on the first day of June, 1868.</code> \| <code>Share Treaty of Greenville. The Treaty of Greenville was signed August 3, 1795, between the United States, represented by Gen. Anthony Wayne, and chiefs of the Indian tribes located in the Northwest Territory, including the Wyandots, Delawares, Shawnees, Ottawas, Miamis, and others.</code> \|
	* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1

	* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
	* Size: 11,662,655 evaluation samples
	* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \| negative \|
	\|:--------\|:--------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 9.2 tokens</li><li>max: 27 tokens</li></ul> \| <ul><li>min: 21 tokens</li><li>mean: 80.44 tokens</li><li>max: 241 tokens</li></ul> \| <ul><li>min: 23 tokens</li><li>mean: 80.38 tokens</li><li>max: 239 tokens</li></ul> \|
	* Samples:
	\| query \| positive \| negative \|
	\|:------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>what county is holly springs nc in</code> \| <code>Holly Springs, North Carolina. Holly Springs is a town in Wake County, North Carolina, United States. As of the 2010 census, the town population was 24,661, over 2Â½ times its population in 2000. Contents.</code> \| <code>The Mt. Holly Springs Park & Resort. One of the numerous trolley routes that carried people around the county at the turn of the century was the Carlisle & Mt. Holly Railway Company. The âHolly Trolleyâ as it came to be known was put into service by Patricio Russo and made its first run on May 14, 1901.</code> \|
	\| <code>how long does nyquil stay in your system</code> \| <code>In order to understand exactly how long Nyquil lasts, it is absolutely vital to learn about the various ingredients in the drug. One of the ingredients found in Nyquil is Doxylamine, which is an antihistamine. This specific medication has a biological half-life or 6 to 12 hours. With this in mind, it is possible for the drug to remain in the system for a period of 12 to 24 hours. It should be known that the specifics will depend on a wide variety of different factors, including your age and metabolism.</code> \| <code>I confirmed that NyQuil is about 10% alcohol, a higher content than most domestic beers. When I asked about the relatively high proof, I was told that the alcohol dilutes the active ingredients. The alcohol free version is there for customers with addiction issues.. also found that in that version there is twice the amount of DXM. When I asked if I could speak to a chemist or scientist, I was told they didn't have anyone who fit that description there. Itâs been eight years since I kicked NyQuil. I've been sober from alcohol for four years.</code> \|
	\| <code>what are mineral water</code> \| <code>1 Mineral water â water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source. Mineral water â water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source.</code> \| <code>Minerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.inerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.</code> \|
	* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `per_device_train_batch_size`: 512
	- `per_device_eval_batch_size`: 512
	- `learning_rate`: 8e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.05
	- `bf16`: 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`: 512
	- `per_device_eval_batch_size`: 512
	- `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`: 8e-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`: 0.05
	- `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`: True
	- `fp16`: False
	- `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
	\| Epoch \| Step \| Training Loss \| msmarco-co-condenser-dev_cosine_accuracy \|
	\|:------:\|:----:\|:-------------:\|:----------------------------------------:\|
	\| 0 \| 0 \| - \| 0.602 \|
	\| 0.2048 \| 500 \| 0.4751 \| - \|
	\| 0.4095 \| 1000 \| 0.1059 \| - \|
	\| 0.6143 \| 1500 \| 0.0771 \| - \|
	\| 0.8190 \| 2000 \| 0.06 \| - \|
	\| 1.0 \| 2442 \| - \| 0.991 \|


	### Framework Versions
	- Python: 3.11.10
	- Sentence Transformers: 3.3.1
	- Transformers: 4.48.0.dev0
	- PyTorch: 2.4.1+cu124
	- Accelerate: 0.26.0
	- 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",
	}
	```

	#### CachedMultipleNegativesRankingLoss
	```bibtex
	@misc{gao2021scaling,
	title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
	author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
	year={2021},
	eprint={2101.06983},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->