README.md

metadata

language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:197462
  - loss:MSELoss
base_model: Qwen/Qwen3-Embedding-0.6B
widget:
  - source_sentence: >-
      Instruct: Given a web search query, retrieve relevant passages that answer
      the query

      Query:who sings the song i don't want to work
    sentences:
      - >-
        The Invisible Man Griffin is the surname of the story's protagonist. His
        name is not mentioned until about halfway through the book. Consumed
        with his greed for power and fame, he is the model of science without
        humanity. A gifted young student, he becomes interested in the science
        of refraction. During his experiments, he accidentally discovers
        chemicals (combined with an unspecified kind of radiation) that would
        make living tissue invisible. Obsessed with his discovery, he tries the
        experiment on himself and becomes invisible. However, he does not know
        how to reverse the process, and he slowly discovers that the advantages
        of being invisible do not outweigh the disadvantages and the problems he
        faces. Thus begins his downfall as he takes the road to crime for his
        survival, revealing in the process his lack of conscience, inhumanity
        and complete selfishness. He progresses from obsession to fanaticism, to
        insanity, and finally to his fateful end.
      - >-
        Instruct: Given a web search query, retrieve relevant passages that
        answer the query

        Query:who did the united states become independent from
      - >-
        Jordan Belfort Jordan Ross Belfort (/ˈbɛlfɔːrt/; born July 9, 1962) is
        an American author, motivational speaker, and former stockbroker. In
        1999, he pleaded guilty to fraud and related crimes in connection with
        stock-market manipulation and running a boiler room as part of a
        penny-stock scam. Belfort spent 22 months in prison as part of an
        agreement under which he gave testimony against numerous partners and
        subordinates in his fraud scheme.[5] He published the memoir The Wolf of
        Wall Street, which was adapted into a film and released in 2013.
  - source_sentence: >-
      London water supply infrastructure Most of London's water comes from
      non-tidal parts of the Thames and Lea, with the remainder being abstracted
      from underground sources.[22]
    sentences:
      - >-
        Instruct: Given a web search query, retrieve relevant passages that
        answer the query

        Query:what is the number on the hogwarts express
      - >-
        Instruct: Given a web search query, retrieve relevant passages that
        answer the query

        Query:when did roughing the kicker become a rule
      - >-
        Agora Early in Greek history (18th century–8th century BC), free-born
        citizens would gather in the agora for military duty or to hear
        statements of the ruling king or council. Later, the Agora also served
        as a marketplace where merchants kept stalls or shops to sell their
        goods amid colonnades. This attracted artisans who built workshops
        nearby.[2]
  - source_sentence: >-
      Instruct: Given a web search query, retrieve relevant passages that answer
      the query

      Query:what is meant by lagging and leading current in ac circuit
    sentences:
      - >-
        .org The domain name org is a generic top-level domain (gTLD) of the
        Domain Name System (DNS) used in the Internet. The name is truncated
        from organization. It was one of the original domains established in
        1985, and has been operated by the Public Interest Registry since 2003.
        The domain was originally intended for non-profit entities, but this
        restriction was not enforced and has been removed. The domain is
        commonly used by schools, open-source projects, and communities, but
        also by some for-profit entities. The number of registered domains in
        org has increased from fewer than one million in the 1990s, to ten
        million as of June 2013.
      - >-
        Instruct: Given a web search query, retrieve relevant passages that
        answer the query

        Query:how many episode in season 1 game of thrones
      - >-
        Instruct: Given a web search query, retrieve relevant passages that
        answer the query

        Query:when is season 11 of doctor who coming out
  - source_sentence: >-
      Gabriel Vlad (born April 9, 1969) in Bucharest, is a former Romanian
      former rugby union football player.
    sentences:
      - >-
        As of May 2013, The Jewish Tribune had a circulation of 60,500 copies a
        week which made it, for a time, the largest Jewish weekly publication in
        Canada.
      - >-
        Cunjamba Dima is a city and commune of Angola, located in the province
        of Cuando Cubango.
      - >-
        He also acted in the National award winning Tamil movie Vazhakku Enn
        18/9, directed by Balaji Sakthivel.
  - source_sentence: The actress was thirteen when she was offered the role of Annie.
    sentences:
      - >-
        All profits from the sale and streaming of the song go to music
        education supported by the CMA Foundation.
      - >-
        Narsingh Temple is situated at the across of the village just across
        confluence of Magri State village.
      - >-
        Contrasting significantly from other soccer leagues in the U.S., WLS
        intends to be an open entry, promotion and relegation competition.
datasets:
  - sentence-transformers/natural-questions
  - sentence-transformers/gooaq
  - sentence-transformers/wikipedia-en-sentences
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
  - negative_mse
model-index:
  - name: SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.26
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.54
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.62
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.74
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.124
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07400000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.54
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.62
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.74
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.49705652353860524
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4194365079365079
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.43104169907220663
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: cosine_accuracy@1
            value: 0.32
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.44
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.46
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.56
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.32
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2533333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.192
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.15600000000000003
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.029912973644699657
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.04555227289257262
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.05270229388942461
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.07692701147361766
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.20504617696332558
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3906269841269841
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.07524365929088889
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.24
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.46
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.62
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.72
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.24
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.12400000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07600000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.23
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.45
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.58
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.68
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.44938843799218575
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3822460317460316
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3789963914205589
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.2733333333333334
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.48
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5666666666666668
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6733333333333333
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2733333333333334
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.19555555555555557
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1466666666666667
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10200000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.17330432454823322
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.34518409096419084
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.41756743129647483
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.4989756704912059
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3838303794980389
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.39743650793650787
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2950939165945515
            name: Cosine Map@100
      - task:
          type: knowledge-distillation
          name: Knowledge Distillation
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: negative_mse
            value: -0.04732320085167885
            name: Negative Mse

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the nq 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: Qwen/Qwen3-Embedding-0.6B
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • nq
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: Qwen3Model 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, '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': True, 'include_prompt': True})
  (2): Normalize()
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/Qwen3-Embedding-0.6B-10-layers")
# Run inference
sentences = [
    'The actress was thirteen when she was offered the role of Annie.',
    'Contrasting significantly from other soccer leagues in the U.S., WLS intends to be an open entry, promotion and relegation competition.',
    'Narsingh Temple is situated at the across of the village just across confluence of Magri State village.',
]
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]

Evaluation

Metrics

Information Retrieval

• Datasets: NanoMSMARCO, NanoNFCorpus and NanoNQ
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "query_prompt": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:"
  }
  

Metric	NanoMSMARCO	NanoNFCorpus	NanoNQ
cosine_accuracy@1	0.26	0.32	0.24
cosine_accuracy@3	0.54	0.44	0.46
cosine_accuracy@5	0.62	0.46	0.62
cosine_accuracy@10	0.74	0.56	0.72
cosine_precision@1	0.26	0.32	0.24
cosine_precision@3	0.18	0.2533	0.1533
cosine_precision@5	0.124	0.192	0.124
cosine_precision@10	0.074	0.156	0.076
cosine_recall@1	0.26	0.0299	0.23
cosine_recall@3	0.54	0.0456	0.45
cosine_recall@5	0.62	0.0527	0.58
cosine_recall@10	0.74	0.0769	0.68
cosine_ndcg@10	0.4971	0.205	0.4494
cosine_mrr@10	0.4194	0.3906	0.3822
cosine_map@100	0.431	0.0752	0.379

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with NanoBEIREvaluator with these parameters:

  {
      "dataset_names": [
          "msmarco",
          "nfcorpus",
          "nq"
      ],
      "query_prompts": {
          "msmarco": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
          "nfcorpus": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
          "nq": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:"
      }
  }
  

Metric	Value
cosine_accuracy@1	0.2733
cosine_accuracy@3	0.48
cosine_accuracy@5	0.5667
cosine_accuracy@10	0.6733
cosine_precision@1	0.2733
cosine_precision@3	0.1956
cosine_precision@5	0.1467
cosine_precision@10	0.102
cosine_recall@1	0.1733
cosine_recall@3	0.3452
cosine_recall@5	0.4176
cosine_recall@10	0.499
cosine_ndcg@10	0.3838
cosine_mrr@10	0.3974
cosine_map@100	0.2951

Knowledge Distillation

• Evaluated with MSEEvaluator

Metric	Value
negative_mse	-0.0473

Training Details

Training Dataset

nq

• Dataset: nq at f9e894e
• Size: 197,462 training samples
• Columns: text and label
• Approximate statistics based on the first 1000 samples:

  	text	label
  type	string	list
  details	min: 27 tokens mean: 89.38 tokens max: 505 tokens	size: 1024 elements

• Samples:

  text	label
  Instruct: Given a web search query, retrieve relevant passages that answer the query Query:the movie bernie based on a true story	[-0.05126953125, -0.0020294189453125, 0.00152587890625, 0.060791015625, 0.022216796875, ...]
  College World Series The College World Series, or CWS, is an annual June baseball tournament held in Omaha, Nebraska. The CWS is the culmination of the National Collegiate Athletic Association (NCAA) Division I Baseball Championship tournament—featuring 64 teams in the first round—which determines the NCAA Division I college baseball champion. The eight participating teams are split into two, four-team, double-elimination brackets, with the winners of each bracket playing in a best-of-three championship series.	[0.033935546875, -0.0908203125, -0.010498046875, 0.0625, -0.01263427734375, ...]
  Instruct: Given a web search query, retrieve relevant passages that answer the query Query:does the femoral nerve turn into the saphenous nerve	[0.052978515625, -0.0028228759765625, -0.0022430419921875, 0.0732421875, 0.044677734375, ...]

• Loss: MSELoss

Evaluation Datasets

nq

• Dataset: nq at f9e894e
• Size: 3,000 evaluation samples
• Columns: text and label
• Approximate statistics based on the first 1000 samples:

  	text	label
  type	string	list
  details	min: 21 tokens mean: 87.24 tokens max: 410 tokens	size: 1024 elements

• Samples:

  text	label
  Instruct: Given a web search query, retrieve relevant passages that answer the query Query:who was the heir apparent of the austro-hungarian empire in 1914	[0.0262451171875, 0.0556640625, -0.0, -0.03076171875, -0.05712890625, ...]
  Instruct: Given a web search query, retrieve relevant passages that answer the query Query:who played tommy in coward of the county	[-0.00848388671875, -0.02294921875, -0.00182342529296875, 0.060546875, -0.021240234375, ...]
  Vertebra The vertebral arch is formed by pedicles and laminae. Two pedicles extend from the sides of the vertebral body to join the body to the arch. The pedicles are short thick processes that extend, one from each side, posteriorly, from the junctions of the posteriolateral surfaces of the centrum, on its upper surface. From each pedicle a broad plate, a lamina, projects backwards and medialwards to join and complete the vertebral arch and form the posterior border of the vertebral foramen, which completes the triangle of the vertebral foramen.[6] The upper surfaces of the laminae are rough to give attachment to the ligamenta flava. These ligaments connect the laminae of adjacent vertebra along the length of the spine from the level of the second cervical vertebra. Above and below the pedicles are shallow depressions called vertebral notches (superior and inferior). When the vertebrae articulate the notches align with those on adjacent vertebrae and these form the openings of the int...	[0.062255859375, -0.005706787109375, -0.009765625, 0.035400390625, -0.0125732421875, ...]

• Loss: MSELoss

gooaq

• Dataset: gooaq at b089f72
• Size: 3,000 evaluation samples
• Columns: text and label
• Approximate statistics based on the first 1000 samples:

  	text	label
  type	string	list
  details	min: 10 tokens mean: 43.88 tokens max: 117 tokens	size: 1024 elements

• Samples:

  text	label
  Instruct: Given a web search query, retrieve relevant passages that answer the query Query:what essential oils are soothing?	[-0.025146484375, 0.06591796875, -0.0025634765625, 0.0732421875, -0.046630859375, ...]
  Titles of books should be underlined or put in italics . (Titles of stories, essays and poems are in "quotation marks.") Refer to the text specifically as a novel, story, essay, memoir, or poem, depending on what it is.	[-0.006988525390625, -0.050537109375, -0.007476806640625, -0.07177734375, -0.049560546875, ...]
  Dakine Cyclone Wet/Dry 32L Backpack. Born from the legacy of our most iconic surf pack, the Cyclone Collection is a family of super-technical and durable wet/dry packs and bags.	[0.0016632080078125, 0.04150390625, -0.01324462890625, 0.0234375, 0.03173828125, ...]

• Loss: MSELoss

wikipedia

• Dataset: wikipedia at 4a0972d
• Size: 3,000 evaluation samples
• Columns: text and label
• Approximate statistics based on the first 1000 samples:

  	text	label
  type	string	list
  details	min: 5 tokens mean: 28.1 tokens max: 105 tokens	size: 1024 elements

• Samples:

  text	label
  The daughter of Vice-admiral George Davies and Julia Hume, she spent her younger years on board the ship he was stationed, the Griper.	[0.0361328125, 0.01904296875, -0.003662109375, 0.0247802734375, 0.0140380859375, ...]
  The impetus for the project began when Amalgamated Dynamics, hired to provide the practical effects for The Thing, a prequel to John Carpenter's 1982 classic film-renowned for its almost exclusive use of practical effects-became disillusioned upon discovering the theatrical release had the bulk of their effects digitally replaced with computer-generated imagery.	[-0.0106201171875, -0.0439453125, -0.01104736328125, 0.00946044921875, 0.0322265625, ...]
  Lost Angeles, his second feature film, starring Joelle Carter and Kelly Blatz, had its world premiere at the Oldenburg International Film Festival in 2012.	[0.0272216796875, 0.0263671875, -0.007110595703125, 0.0294189453125, 0.01129150390625, ...]

• Loss: MSELoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• learning_rate: 0.0001
• num_train_epochs: 1
• warmup_ratio: 0.1
• bf16: True
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• 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: 0.0001
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: 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}
• tp_size: 0
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	nq loss	gooaq loss	wikipedia loss	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10	negative_mse
-1	-1	-	-	-	-	0.0	0.0111	0.0	0.0037	-0.1948
0.0162	100	0.0018	-	-	-	-	-	-	-	-
0.0324	200	0.0013	-	-	-	-	-	-	-	-
0.0486	300	0.0012	-	-	-	-	-	-	-	-
0.0648	400	0.0012	-	-	-	-	-	-	-	-
0.0810	500	0.0011	0.0010	0.0012	0.0011	0.0	0.0250	0.0791	0.0347	-0.1091
0.0972	600	0.001	-	-	-	-	-	-	-	-
0.1134	700	0.0009	-	-	-	-	-	-	-	-
0.1296	800	0.0008	-	-	-	-	-	-	-	-
0.1458	900	0.0007	-	-	-	-	-	-	-	-
0.1620	1000	0.0006	0.0006	0.0008	0.0008	0.3983	0.1100	0.3080	0.2721	-0.0706
0.1783	1100	0.0006	-	-	-	-	-	-	-	-
0.1945	1200	0.0005	-	-	-	-	-	-	-	-
0.2107	1300	0.0005	-	-	-	-	-	-	-	-
0.2269	1400	0.0005	-	-	-	-	-	-	-	-
0.2431	1500	0.0005	0.0005	0.0007	0.0006	0.4665	0.1554	0.3481	0.3233	-0.0593
0.2593	1600	0.0005	-	-	-	-	-	-	-	-
0.2755	1700	0.0005	-	-	-	-	-	-	-	-
0.2917	1800	0.0005	-	-	-	-	-	-	-	-
0.3079	1900	0.0004	-	-	-	-	-	-	-	-
0.3241	2000	0.0004	0.0004	0.0006	0.0006	0.4292	0.1827	0.4041	0.3387	-0.0541
0.3403	2100	0.0004	-	-	-	-	-	-	-	-
0.3565	2200	0.0004	-	-	-	-	-	-	-	-
0.3727	2300	0.0004	-	-	-	-	-	-	-	-
0.3889	2400	0.0004	-	-	-	-	-	-	-	-
0.4051	2500	0.0004	0.0004	0.0006	0.0006	0.4780	0.1915	0.4106	0.3600	-0.0515
0.4213	2600	0.0004	-	-	-	-	-	-	-	-
0.4375	2700	0.0004	-	-	-	-	-	-	-	-
0.4537	2800	0.0004	-	-	-	-	-	-	-	-
0.4699	2900	0.0004	-	-	-	-	-	-	-	-
0.4861	3000	0.0004	0.0004	0.0006	0.0005	0.4937	0.1937	0.4117	0.3664	-0.0498
0.5023	3100	0.0004	-	-	-	-	-	-	-	-
0.5186	3200	0.0004	-	-	-	-	-	-	-	-
0.5348	3300	0.0004	-	-	-	-	-	-	-	-
0.5510	3400	0.0004	-	-	-	-	-	-	-	-
0.5672	3500	0.0004	0.0004	0.0005	0.0005	0.4939	0.1955	0.4533	0.3809	-0.0489
0.5834	3600	0.0004	-	-	-	-	-	-	-	-
0.5996	3700	0.0004	-	-	-	-	-	-	-	-
0.6158	3800	0.0004	-	-	-	-	-	-	-	-
0.6320	3900	0.0004	-	-	-	-	-	-	-	-
0.6482	4000	0.0004	0.0004	0.0005	0.0005	0.4948	0.2011	0.4373	0.3777	-0.0482
0.6644	4100	0.0004	-	-	-	-	-	-	-	-
0.6806	4200	0.0004	-	-	-	-	-	-	-	-
0.6968	4300	0.0004	-	-	-	-	-	-	-	-
0.7130	4400	0.0004	-	-	-	-	-	-	-	-
0.7292	4500	0.0004	0.0004	0.0005	0.0005	0.4909	0.2049	0.4515	0.3824	-0.0477
0.7454	4600	0.0004	-	-	-	-	-	-	-	-
0.7616	4700	0.0004	-	-	-	-	-	-	-	-
0.7778	4800	0.0004	-	-	-	-	-	-	-	-
0.7940	4900	0.0004	-	-	-	-	-	-	-	-
0.8102	5000	0.0004	0.0004	0.0005	0.0005	0.4875	0.2022	0.4448	0.3782	-0.0475
0.8264	5100	0.0004	-	-	-	-	-	-	-	-
0.8427	5200	0.0004	-	-	-	-	-	-	-	-
0.8589	5300	0.0004	-	-	-	-	-	-	-	-
0.8751	5400	0.0004	-	-	-	-	-	-	-	-
0.8913	5500	0.0004	0.0004	0.0005	0.0005	0.4943	0.2043	0.4519	0.3835	-0.0474
0.9075	5600	0.0004	-	-	-	-	-	-	-	-
0.9237	5700	0.0004	-	-	-	-	-	-	-	-
0.9399	5800	0.0004	-	-	-	-	-	-	-	-
0.9561	5900	0.0004	-	-	-	-	-	-	-	-
0.9723	6000	0.0004	0.0004	0.0005	0.0005	0.4971	0.205	0.4494	0.3838	-0.0473
0.9885	6100	0.0004	-	-	-	-	-	-	-	-
-1	-1	-	-	-	-	0.4971	0.2050	0.4494	0.3838	-0.0473

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.11.10
• Sentence Transformers: 4.2.0.dev0
• Transformers: 4.51.2
• PyTorch: 2.5.1+cu124
• Accelerate: 1.5.2
• Datasets: 3.5.0
• Tokenizers: 0.21.0

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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}