minilm-msmarco-distilled

MiniLM-L12-H384-uncased reranker distilled from MS MARCO ensemble

This is a Cross Encoder model finetuned from microsoft/MiniLM-L12-H384-uncased on the msmarco dataset using the sentence-transformers 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 <!-- at revision 44acabbec0ef496f6dbc93adadea57f376b7c0ec -->
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label
• Supported Modality: Text
• Training Dataset:
  • msmarco
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'BertForSequenceClassification'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("Hyperakan/minilm-msmarco-distilled")
# Get scores for pairs of inputs
pairs = [
    ['what are roof rats', 'Rattus rattus redirects here. For the album by Merzbow, see Rattus Rattus (album). The black rat (Rattus rattus), also known as the ship rat, roof rat, house rat, is a common long-tailed rodent of the genus Rattus (rats) in the subfamily Murinae. The species originated in tropical Asia and spread through the Near East in Roman times before reaching Europe by the 1st century and spreading with Europeans across the world.'],
    ['is overeating an eating disorder?', 'Compulsive overeating disorder is frequently called a â\x80\x9cfood addictionâ\x80\x9d because eating can cause biological changes in your body that are similar to those caused by addictive substances. Studies have found that foods, particularly high-fat foods, affect the areas of your brain associated with pleasure.'],
    ['can polycythemia cause stroke', 'Polycythemia Vera Complications. If you have PV, the thickness of your blood and the slowed blood flow can cause serious health problems. Blood clots are the most serious complication of PV. Blood clots can cause a heart attack or stroke. They also can cause your liver and spleen to enlarge.'],
    ['how many years of college does it take to be a pediatrician', 'Training to become a pediatrician requires a four-year undergraduate degree, four years of medical school, and three or more years of hands-on residency training.'],
    ['using commas correctly in sentences', "Commas and periods are the most frequently used punctuation marks. Commas customarily indicate a brief pause; they're not as final as periods. Rule 1. Use commas to separate words and word groups in a simple series of three or more items. Example: My estate goes to my husband, son, daughter-in-law, and nephew. Note: When the last comma in a series comes before and or or (after daughter-in-law in the above example), it is known as the Oxford comma. Most newspapers and magazines drop the Oxford comma in a simple series, apparently feeling it's unnecessary."],
]
scores = model.predict(pairs)
print(scores)
# [-0.0032 -0.0024 -0.0019  0.0005 -0.0017]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what are roof rats',
    [
        'Rattus rattus redirects here. For the album by Merzbow, see Rattus Rattus (album). The black rat (Rattus rattus), also known as the ship rat, roof rat, house rat, is a common long-tailed rodent of the genus Rattus (rats) in the subfamily Murinae. The species originated in tropical Asia and spread through the Near East in Roman times before reaching Europe by the 1st century and spreading with Europeans across the world.',
        'Compulsive overeating disorder is frequently called a â\x80\x9cfood addictionâ\x80\x9d because eating can cause biological changes in your body that are similar to those caused by addictive substances. Studies have found that foods, particularly high-fat foods, affect the areas of your brain associated with pleasure.',
        'Polycythemia Vera Complications. If you have PV, the thickness of your blood and the slowed blood flow can cause serious health problems. Blood clots are the most serious complication of PV. Blood clots can cause a heart attack or stroke. They also can cause your liver and spleen to enlarge.',
        'Training to become a pediatrician requires a four-year undergraduate degree, four years of medical school, and three or more years of hands-on residency training.',
        "Commas and periods are the most frequently used punctuation marks. Commas customarily indicate a brief pause; they're not as final as periods. Rule 1. Use commas to separate words and word groups in a simple series of three or more items. Example: My estate goes to my husband, son, daughter-in-law, and nephew. Note: When the last comma in a series comes before and or or (after daughter-in-law in the above example), it is known as the Oxford comma. Most newspapers and magazines drop the Oxford comma in a simple series, apparently feeling it's unnecessary.",
    ]
)
# [{'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> with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": true
  }
  

Cross Encoder Nano BEIR

• Dataset: NanoBEIR_R100_mean
• Evaluated with <code>CrossEncoderNanoBEIREvaluator</code> with these parameters:

  {
      "dataset_names": [
          "msmarco",
          "nfcorpus",
          "nq"
      ],
      "dataset_id": "sentence-transformers/NanoBEIR-en",
      "rerank_k": 100,
      "at_k": 10,
      "always_rerank_positives": true
  }
  

<!-- ## 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

• Dataset: msmarco
• Size: 100 training samples
• Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 100 samples:

  	score	query	positive	negative
  type	float	string	string	string
  modality		text	text	text
  details	<ul><li>min: 0.6</li><li>mean: 12.6</li><li>max: 20.57</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 9.43 tokens</li><li>max: 16 tokens</li></ul>	<ul><li>min: 13 tokens</li><li>mean: 78.46 tokens</li><li>max: 197 tokens</li></ul>	<ul><li>min: 32 tokens</li><li>mean: 77.94 tokens</li><li>max: 161 tokens</li></ul>

• Samples:

  score	query	positive	negative
  <code>12.03586260477702</code>	<code>obesity synonyms</code>	<code>Synonyms: embonpoint; stoutness; corpulency; exogenous obesity; overweight; avoirdupois; plumpness; fat; fatness; roundness; adiposis; corpulence; blubber; fleshiness; Antonyms: leanness</code>	<code>Make an effort synonyms. Top make an effort synonyms (idioms only) are have a crack, chip away at and give a go.</code>
  <code>8.629784286022186</code>	<code>minecraft 1.9.2 how to make fences</code>	<code>Once you have these you can then put them back into the Crafting Box and make them into Sticks. Place all items like how it is displayed below into the table: Now that you have learned how to make a fence in Minecraft, they are 1.5 blocks tall so nothing is able to jump over them.You are able to place blocks on top of the fence that will sit on the fence like it is one block. Fences can be used as a Fuel Source in Furnaces.A Fence Gate is commonly used with fences so a player easily go through a wall of fences.ow To Make A Fence In Minecraft. Crafting is the way to make a cake in Minecraft. The player will first need one crafting table. It can be made by opening your inventory box and placing 4 Wooden Planks into the 2×2 grid. If you just made the crafting table then you will need to equip it and place it on the ground.</code>	<code>1.9, the first release of the Combat Update, is an upcoming major update to Minecraft with no set release date.Dinnerbone has stated in May that many of the features he has shown for 1.9 are not “100% final”, so things may change later.or other editions, see Version history (disambiguation) . 1.9, the first release of the Combat Update, is an upcoming major update to Minecraft with no set release date. Dinnerbone has stated in May that many of the features he has shown for 1.9 are not “100% final”, so things may change later.</code>
  <code>8.286005536715189</code>	<code>what is trap it term</code>	<code>Trap (computing) In computing and operating systems, a trap, also known as an exception or a fault, is typically[NB 1][1] a type of synchronous interrupt typically caused by an exceptional condition (e.g., breakpoint, division by zero, invalid memory access).</code>	<code>1 A cage trap baited with peanut butter or a sticky trap that is of a size designed to catch rats will get them while in the car. 2 Then drown the rats or suffocate them in a plastic bag (trap and rat go in the bag) and then trash them. A cage trap baited with peanut butter or a sticky trap that is of a size designed to catch rats will get them while in the car. 2 Then drown the rats or suffocate them in a plastic bag (trap and rat go in the bag) and then trash them.</code>

• Loss: <code>MarginMSELoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity"
  }
  

Evaluation Dataset

msmarco

• Dataset: msmarco
• Size: 20 evaluation samples
• Columns: <code>score</code>, <code>query</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 20 samples:

  	score	query	positive	negative
  type	float	string	string	string
  modality		text	text	text
  details	<ul><li>min: -1.83</li><li>mean: 12.68</li><li>max: 20.24</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 8.4 tokens</li><li>max: 15 tokens</li></ul>	<ul><li>min: 34 tokens</li><li>mean: 89.75 tokens</li><li>max: 147 tokens</li></ul>	<ul><li>min: 32 tokens</li><li>mean: 78.8 tokens</li><li>max: 161 tokens</li></ul>

• Samples:

  score	query	positive	negative
  <code>16.17283757527669</code>	<code>what are roof rats</code>	<code>Rattus rattus redirects here. For the album by Merzbow, see Rattus Rattus (album). The black rat (Rattus rattus), also known as the ship rat, roof rat, house rat, is a common long-tailed rodent of the genus Rattus (rats) in the subfamily Murinae. The species originated in tropical Asia and spread through the Near East in Roman times before reaching Europe by the 1st century and spreading with Europeans across the world.</code>	<code>1 A cage trap baited with peanut butter or a sticky trap that is of a size designed to catch rats will get them while in the car. 2 Then drown the rats or suffocate them in a plastic bag (trap and rat go in the bag) and then trash them. A cage trap baited with peanut butter or a sticky trap that is of a size designed to catch rats will get them while in the car. 2 Then drown the rats or suffocate them in a plastic bag (trap and rat go in the bag) and then trash them.</code>
  <code>12.888915936152141</code>	<code>is overeating an eating disorder?</code>	<code>Compulsive overeating disorder is frequently called a “food addiction” because eating can cause biological changes in your body that are similar to those caused by addictive substances. Studies have found that foods, particularly high-fat foods, affect the areas of your brain associated with pleasure.</code>	<code>Emetrol is an oral solution designed to soothe the stomach when nausea and vomiting are caused by a viral or bacterial infection or overeating. Emetrol contains sugar and phosphoric acid. Diabetics should not use Emetrol without medical supervision because of the concentrated sugar.</code>
  <code>18.19395383199056</code>	<code>can polycythemia cause stroke</code>	<code>Polycythemia Vera Complications. If you have PV, the thickness of your blood and the slowed blood flow can cause serious health problems. Blood clots are the most serious complication of PV. Blood clots can cause a heart attack or stroke. They also can cause your liver and spleen to enlarge.</code>	<code>Stroke Recovery. Stroke affects everybody differently. Many stroke survivors continue to improve over a long time, sometimes over a number of years. Recovery from stroke involves making changes in the physical, social and, emotional aspects of your life. You will make changes to prevent additional strokes as well as to facilitate your life-long recovery.</code>

• Loss: <code>MarginMSELoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• learning_rate: 8e-06
• weight_decay: 0.01
• 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
• 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: 8e-06
• weight_decay: 0.01
• 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: None
• 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
• 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• hub_revision: None
• 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: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Training Time

• Training: 1.0 minutes
• Evaluation: 3.1 minutes
• Total: 4.1 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.6.0
• Transformers: 4.57.6
• PyTorch: 2.6.0+cu124
• Accelerate: 1.14.0
• Datasets: 5.0.0
• Tokenizers: 0.22.2

Additional Resources

• Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

Citation

BibTeX

Sentence Transformers

@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",
}

MarginMSELoss

@misc{hofstätter2021improving,
    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
    year={2021},
    eprint={2010.02666},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}

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