Add new CrossEncoder model

b751c7c verified 19 days ago

41.7 kB

	---
	language:
	- en
	tags:
	- sentence-transformers
	- cross-encoder
	- generated_from_trainer
	- dataset_size:78704
	- loss:PListMLELoss
	base_model: microsoft/MiniLM-L12-H384-uncased
	datasets:
	- microsoft/ms_marco
	pipeline_tag: text-ranking
	library_name: sentence-transformers
	metrics:
	- map
	- mrr@10
	- ndcg@10
	co2_eq_emissions:
	emissions: 92.04622402434568
	energy_consumed: 0.23680409162892313
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.766
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
	results:
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoMSMARCO R100
	type: NanoMSMARCO_R100
	metrics:
	- type: map
	value: 0.4782
	name: Map
	- type: mrr@10
	value: 0.4685
	name: Mrr@10
	- type: ndcg@10
	value: 0.5464
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoNFCorpus R100
	type: NanoNFCorpus_R100
	metrics:
	- type: map
	value: 0.3347
	name: Map
	- type: mrr@10
	value: 0.5293
	name: Mrr@10
	- type: ndcg@10
	value: 0.358
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoNQ R100
	type: NanoNQ_R100
	metrics:
	- type: map
	value: 0.6353
	name: Map
	- type: mrr@10
	value: 0.6425
	name: Mrr@10
	- type: ndcg@10
	value: 0.6876
	name: Ndcg@10
	- task:
	type: cross-encoder-nano-beir
	name: Cross Encoder Nano BEIR
	dataset:
	name: NanoBEIR R100 mean
	type: NanoBEIR_R100_mean
	metrics:
	- type: map
	value: 0.4827
	name: Map
	- type: mrr@10
	value: 0.5468
	name: Mrr@10
	- type: ndcg@10
	value: 0.5307
	name: Ndcg@10
	---

	# CrossEncoder based on microsoft/MiniLM-L12-H384-uncased

	This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

	## Model Details

	### Model Description
	- Model Type: Cross Encoder
	- Base model: [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) <!-- at revision 44acabbec0ef496f6dbc93adadea57f376b7c0ec -->
	- Maximum Sequence Length: 512 tokens
	- Number of Output Labels: 1 label
	- Training Dataset:
	- [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco)
	- Language: en
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Documentation: [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)

	## 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 CrossEncoder

	# Download from the 🤗 Hub
	model = CrossEncoder("tomaarsen/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-plistmle-sum-to-1-weight-plus-1")
	# Get scores for pairs of texts
	pairs = [
	['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
	['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
	['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
	]
	scores = model.predict(pairs)
	print(scores.shape)
	# (3,)

	# Or rank different texts based on similarity to a single text
	ranks = model.rank(
	'How many calories in an egg',
	[
	'There are on average between 55 and 80 calories in an egg depending on its size.',
	'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
	'Most of the calories in an egg come from the yellow yolk in the center.',
	]
	)
	# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
	```

	<!--
	### 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

	#### Cross Encoder Reranking

	* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
	* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
	```json
	{
	"at_k": 10,
	"always_rerank_positives": true
	}
	```

	\| Metric \| NanoMSMARCO_R100 \| NanoNFCorpus_R100 \| NanoNQ_R100 \|
	\|:------------\|:---------------------\|:---------------------\|:---------------------\|
	\| map \| 0.4782 (-0.0114) \| 0.3347 (+0.0737) \| 0.6353 (+0.2157) \|
	\| mrr@10 \| 0.4685 (-0.0090) \| 0.5293 (+0.0294) \| 0.6425 (+0.2158) \|
	\| ndcg@10 \| 0.5464 (+0.0060) \| 0.3580 (+0.0330) \| 0.6876 (+0.1870) \|

	#### Cross Encoder Nano BEIR

	* Dataset: `NanoBEIR_R100_mean`
	* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
	```json
	{
	"dataset_names": [
	"msmarco",
	"nfcorpus",
	"nq"
	],
	"rerank_k": 100,
	"at_k": 10,
	"always_rerank_positives": true
	}
	```

	\| Metric \| Value \|
	\|:------------\|:---------------------\|
	\| map \| 0.4827 (+0.0927) \|
	\| mrr@10 \| 0.5468 (+0.0788) \|
	\| ndcg@10 \| 0.5307 (+0.0753) \|

	<!--
	## 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

	#### ms_marco

	* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
	* Size: 78,704 training samples
	* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| docs \| labels \|
	\|:--------\|:-----------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------\|
	\| type \| string \| list \| list \|
	\| details \| <ul><li>min: 9 characters</li><li>mean: 33.83 characters</li><li>max: 100 characters</li></ul> \| <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> \| <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> \|
	* Samples:
	\| query \| docs \| labels \|
	\|:-------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------\|
	\| <code>cost for taking care of elderly parents at home</code> \| <code>['Google+. Learn about dealing with and caring for your elderly parents as they age. When it comes to caring for elderly parents, there are a number of options out there. Your first decision is where your parent will live – in his or her own home, with you, or in an elder care facility. The cost of elder care can be daunting, and for some families out-of-home care is simply an impossibility. According to the 2011 MetLife Market Survey of Long-Term Care Costs, the cost of caring for aging parents is the following: 1 Nursing home (Semi-private room): $214/day or $78,110 per year', "The cost to care for a parent in your home can vary depending on their needs. You can expect to pay between $15 and $25 per hour for home care personnel and $300+ per day for round the clock care (live-in) Some people utilize housekeepers and/or family members to bring the cost down. Some caregivers have to decrease work hours or even quit their job in order to provide care for an aging parent, and when you a...</code> \| <code>[1, 1, 0, 0, 0, ...]</code> \|
	\| <code>what is a pharmacist</code> \| <code>['pharmacist, n a person prepared to formulate and dispense drugs or medications through completion of an accredited university program in pharmacy. Licensure is required upon completion of the program and prior to serving the public as a pharma-cist.', 'Pharmacists, also known as chemists (Commonwealth English) or druggists (North American and, archaically, Commonwealth English), are healthcare professionals who practice in pharmacy, the field of health sciences focusing on safe and effective medication use.', 'The most common pharmacist positions are that of a community pharmacist (also referred to as a retail pharmacist, first-line pharmacist or dispensing chemist), or a hospital pharmacist, where they instruct and counsel on the proper use and adverse effects of medically prescribed drugs and medicines.', 'A pharmacist is a member of the health care team directly involved with patient care. Pharmacists undergo university-level education to understand the biochemical mechanisms and ...</code> \| <code>[1, 0, 0, 0, 0, ...]</code> \|
	\| <code>carissa name meaning</code> \| <code>['Greek Meaning: The name Carissa is a Greek baby name. In Greek the meaning of the name Carissa is: Very dear. American Meaning: The name Carissa is an American baby name. In American the meaning of the name Carissa is: Very dear. Latin Meaning: The name Carissa is a Latin baby name. In Latin the meaning of the name Carissa is: Artistic or giving; Very dear.', 'The meaning of Carissa has more than one different etymologies. It has same or different meanings in other countries and languages. The different meanings of the name Carissa are: 1 French Meaning: Caress. Form of: Caressa. Keep in mind that many names may have different meanings in other countries and languages, so be careful that the name that you choose doesn’t mean something bad or unpleasant. Search comprehensively and find the name meaning of Carissa and its name origin or of any other name in our database. Also note the spelling and the pronunciation of the name Carissa and check the initials of the name with your last ...</code> \| <code>[1, 0, 0, 0, 0, ...]</code> \|
	* Loss: [<code>PListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#plistmleloss) with these parameters:
	```json
	{
	"lambda_weight": "sentence_transformers.cross_encoder.losses.PListMLELoss.PListMLELambdaWeight",
	"activation_fct": "torch.nn.modules.linear.Identity",
	"mini_batch_size": 16,
	"respect_input_order": true
	}
	```

	### Evaluation Dataset

	#### ms_marco

	* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
	* Size: 1,000 evaluation samples
	* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| docs \| labels \|
	\|:--------\|:----------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------\|
	\| type \| string \| list \| list \|
	\| details \| <ul><li>min: 11 characters</li><li>mean: 33.1 characters</li><li>max: 88 characters</li></ul> \| <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> \| <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> \|
	* Samples:
	\| query \| docs \| labels \|
	\|:---------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------\|
	\| <code>what county is nagold in</code> \| <code>["detailed map of Nagold and near places. Welcome to the Nagold google satellite map! This place is situated in Calw, Karlsruhe, Baden-Wurttemberg, Germany, its geographical coordinates are 48° 33' 0 North, 8° 43' 0 East and its original name (with diacritics) is Nagold. See Nagold photos and images from satellite below, explore the aerial photographs of Nagold in Germany. 3D map of Nagold in Germany. You can also dive right into Nagold on unique 3D satellite map provided by Google Earth. With new GoogLe Earth plugin you can enjoy the interactive Nagold 3D map within your web browser.", 'The location of each Nagold hotel listed is shown on the detailed zoomable map. Moreover, Nagold hotel map is available where all hotels in Nagold are marked. You can easily choose your hotel by location. 3D map of Nagold in Germany. You can also dive right into Nagold on unique 3D satellite map provided by Google Earth. With new GoogLe Earth plugin you can enjoy the interactive Nagold 3D map within your web browser.', "Jonagold is high quality American apple, developed in the 1940s. As its name suggests, this is a cross between a Jonathan and a Golden Delicious. It is quite widely grown, and unusually for a Golden Delicious cross, is not limited to the warm apple regions, although it is not often found in the UK. The colouring is yellow of Golden Delicious, with large flushes of red. This is a crisp apple to bite into, with gleaming white flesh. The flavour is sweet but with a lot of balancing acidity-a very pleasant apple. Jonagold's other parent, Jonathan, is an old American variety which was discovered in the 1820s.", "Jonagold is widely-grown by commercial growers, and there are a number of more highly-coloured sports. Jonagored is probably the most widely known of these. Others include: Decosta, Primo, Rubinstar, Red Jonaprince. The colouring is yellow of Golden Delicious, with large flushes of red. This is a crisp apple to bite into, with gleaming white flesh. The flavour is sweet but with a lot of balancing acidity-a very pleasant apple. Jonagold's other parent, Jonathan, is an old American variety which was discovered in the 1820s.", "As a result it is a poor pollinator of other apple varieties, and needs two different nearby compatible pollinating apple varieties. Golden Delicious is well-known as a good pollinator of other apple varieties, but cannot pollinate Jonagold. The colouring is yellow of Golden Delicious, with large flushes of red. This is a crisp apple to bite into, with gleaming white flesh. The flavour is sweet but with a lot of balancing acidity-a very pleasant apple. Jonagold's other parent, Jonathan, is an old American variety which was discovered in the 1820s. In the UK Jonagold sometimes appears in supermarkets in the spring packaged as value apples, often from Holland, and at a very low price"]</code> \| <code>[1, 0, 0, 0, 0]</code> \|
	\| <code>what is the pay scale for ups drivers</code> \| <code>["According to American job and career site Glassdoor, the average salary of a UPS truck driver is about $56,000 a year. What is the average salary for a ups driver? currently in southern California its 29.71/hr plus full medical, dental and vision. also paid are a legal plan, pension, major holiday's off paid, and up to 6 weeks paid vacat … ion and 4 floating holidays. also yearly guaranteed prenegotiated raises.", 'Average UPS Driver salaries for job postings nationwide are 57% lower than average salaries for all job postings nationwide. ', "I was told that the starting salary for a full time package delivery driver is $72,000/year and are in the midst of negotiations for a salary increase to $75,000/ year. Is this true? I appologize I am new but am curious as I would like to, some day, obtain a full time driver position. 70K is gross pay for a senior Package Driver in my area that averages working 9.5 hours a day. That is $28.19 an hour straight pay and about $42.38 an hour for over...</code> \| <code>[1, 0, 0, 0, 0, ...]</code> \|
	\| <code>what is coliform</code> \| <code>['Coliform is a rod-shaped bacteria which are always present in the digestive tract of warm-blooded animals, including humans. Coliforms are found in human and animal waste, and are also found in water, plants and soil. Fecal coliforms are the coliform that is found in the digestive tract of animals and humans, and is an indicator of fecal contamination. A high count of fecal coliforms is considered an accurate indicator of animal or human waste. Escherichia coli (E. coli) is a major species of the fecal coliforms', 'Coliforms are a broad class of bacteria found in our environment, including the feces of man and other warm-blooded animals. The presence of coliform bacteria in drinking water may indicate a possible presence of harmful, disease-causing organisms. Why use coliforms to indicate water quality? ', 'Coliform bacteria include a large group of many types of bacteria that occur throughout the environment. They are common in soil and surface water and may even occur on your skin....</code> \| <code>[1, 0, 0, 0, 0, ...]</code> \|
	* Loss: [<code>PListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#plistmleloss) with these parameters:
	```json
	{
	"lambda_weight": "sentence_transformers.cross_encoder.losses.PListMLELoss.PListMLELambdaWeight",
	"activation_fct": "torch.nn.modules.linear.Identity",
	"mini_batch_size": 16,
	"respect_input_order": true
	}
	```

	### 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
	- `seed`: 12
	- `bf16`: True
	- `load_best_model_at_end`: True

	#### 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`: 12
	- `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`: True
	- `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`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_R100_ndcg@10 \| NanoNFCorpus_R100_ndcg@10 \| NanoNQ_R100_ndcg@10 \| NanoBEIR_R100_mean_ndcg@10 \|
	\|:----------:\|:--------:\|:-------------:\|:---------------:\|:------------------------:\|:-------------------------:\|:--------------------:\|:--------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.0293 (-0.5112) \| 0.2960 (-0.0290) \| 0.0263 (-0.4743) \| 0.1172 (-0.3382) \|
	\| 0.0002 \| 1 \| 2.2004 \| - \| - \| - \| - \| - \|
	\| 0.0508 \| 250 \| 2.1006 \| - \| - \| - \| - \| - \|
	\| 0.1016 \| 500 \| 1.9618 \| 1.9431 \| 0.0989 (-0.4415) \| 0.3091 (-0.0159) \| 0.1591 (-0.3416) \| 0.1890 (-0.2663) \|
	\| 0.1525 \| 750 \| 1.9038 \| - \| - \| - \| - \| - \|
	\| 0.2033 \| 1000 \| 1.8656 \| 1.8699 \| 0.4825 (-0.0579) \| 0.3387 (+0.0136) \| 0.5589 (+0.0582) \| 0.4600 (+0.0046) \|
	\| 0.2541 \| 1250 \| 1.8568 \| - \| - \| - \| - \| - \|
	\| 0.3049 \| 1500 \| 1.8491 \| 1.8546 \| 0.5522 (+0.0118) \| 0.3395 (+0.0144) \| 0.6046 (+0.1040) \| 0.4988 (+0.0434) \|
	\| 0.3558 \| 1750 \| 1.8455 \| - \| - \| - \| - \| - \|
	\| 0.4066 \| 2000 \| 1.8337 \| 1.8389 \| 0.5163 (-0.0242) \| 0.3749 (+0.0499) \| 0.6346 (+0.1340) \| 0.5086 (+0.0532) \|
	\| 0.4574 \| 2250 \| 1.8433 \| - \| - \| - \| - \| - \|
	\| 0.5082 \| 2500 \| 1.8311 \| 1.8265 \| 0.5464 (+0.0060) \| 0.3580 (+0.0330) \| 0.6876 (+0.1870) \| 0.5307 (+0.0753) \|
	\| 0.5591 \| 2750 \| 1.8157 \| - \| - \| - \| - \| - \|
	\| 0.6099 \| 3000 \| 1.8111 \| 1.8187 \| 0.5313 (-0.0091) \| 0.3582 (+0.0332) \| 0.6415 (+0.1409) \| 0.5103 (+0.0550) \|
	\| 0.6607 \| 3250 \| 1.8183 \| - \| - \| - \| - \| - \|
	\| 0.7115 \| 3500 \| 1.8155 \| 1.8160 \| 0.5417 (+0.0013) \| 0.3751 (+0.0501) \| 0.6412 (+0.1405) \| 0.5193 (+0.0640) \|
	\| 0.7624 \| 3750 \| 1.8101 \| - \| - \| - \| - \| - \|
	\| 0.8132 \| 4000 \| 1.8124 \| 1.8105 \| 0.5468 (+0.0063) \| 0.3616 (+0.0366) \| 0.6357 (+0.1351) \| 0.5147 (+0.0593) \|
	\| 0.8640 \| 4250 \| 1.8158 \| - \| - \| - \| - \| - \|
	\| 0.9148 \| 4500 \| 1.8082 \| 1.8129 \| 0.5380 (-0.0024) \| 0.3564 (+0.0313) \| 0.6629 (+0.1623) \| 0.5191 (+0.0637) \|
	\| 0.9656 \| 4750 \| 1.8042 \| - \| - \| - \| - \| - \|
	\| -1 \| -1 \| - \| - \| 0.5464 (+0.0060) \| 0.3580 (+0.0330) \| 0.6876 (+0.1870) \| 0.5307 (+0.0753) \|

	* The bold row denotes the saved checkpoint.

	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.237 kWh
	- Carbon Emitted: 0.092 kg of CO2
	- Hours Used: 0.766 hours

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

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 3.5.0.dev0
	- Transformers: 4.49.0
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.5.1
	- Datasets: 3.3.2
	- 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",
	}
	```

	#### PListMLELoss
	```bibtex
	@inproceedings{lan2014position,
	title={Position-Aware ListMLE: A Sequential Learning Process for Ranking.},
	author={Lan, Yanyan and Zhu, Yadong and Guo, Jiafeng and Niu, Shuzi and Cheng, Xueqi},
	booktitle={UAI},
	volume={14},
	pages={449--458},
	year={2014}
	}
	```

	<!--
	## 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.
	-->