Gonalb/flucold-ft-v0

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. 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: Snowflake/snowflake-arctic-embed-l
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

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: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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("Gonalb/flucold-ft-v0")
# Run inference
sentences = [
    'QUESTION #1: How might changes in posture from sitting to supine affect sinus pain according to the context?',
    'gas absorption from the sinus and “vacuum maxillary\nsinusitis”.37 However, sinuses with patent ostia may also\nbe painful, indicating that the generation of\ninflammatory mediators within the sinus may be\nsufficient to trigger the sensation of pain either by direct\nstimulation of pain nerve fibres or via distension of blood\nvessels that are also served by sensory nerves.36 Changes\nin posture from sitting to supine cause an increase in\nsinus pain that may be related to dilation of the blood\nvessels draining the sinus caused by an increase in\nvenous pressure. Pressure changes in the sinus may also\ncause pain by stimulation of branches of the trigeminal\nnerve that course in and around the sinuses.37\nWatery eyes',
    'American Indians and Alaska Natives\nChildren younger than 5 years (particularly those younger \nthan 2 years)\nInstitutionalized adults (e.g., residents of nursing homes or \nchronic care facilities)\nPregnant and postpartum women (up to 2 weeks postpartum, \nincluding pregnancy loss)\nAdapted with permission from Erlikh IV, Abraham S, Kondamudi VK. \nManagement of influenza. Am Fam Physician. 2010; 82(9): 1088, with \nadditional information from references 18 and 19.',
]
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

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.75
cosine_accuracy@3	0.9167
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.75
cosine_precision@3	0.3056
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.75
cosine_recall@3	0.9167
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8865
cosine_mrr@10	0.8486
cosine_map@100	0.8486

Training Details

Training Dataset

Unnamed Dataset

• Size: 334 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 334 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 12 tokens mean: 24.85 tokens max: 61 tokens	min: 61 tokens mean: 159.74 tokens max: 248 tokens

• Samples:

  sentence_0	sentence_1
  QUESTION #1: What is the source website from which the document was downloaded?	Downloaded from the American Family Physician website at www.aafp.org/afp. Copyright © 2019 American Academy of Family Physicians. For the private, noncom - mercial use of one individual user of the website. All other rights reserved. Contact [email protected] for copyright questions and/or permission requests.
  QUESTION #2: Who should be contacted for copyright questions and/or permission requests regarding the document?	Downloaded from the American Family Physician website at www.aafp.org/afp. Copyright © 2019 American Academy of Family Physicians. For the private, noncom - mercial use of one individual user of the website. All other rights reserved. Contact [email protected] for copyright questions and/or permission requests.
  QUESTION #1: Why is early diagnosis essential for antiviral therapy and public-health measures in the community?	syndrome (SARS) 3 because early diagnosis is essential for any antiviral therapy and for the initiation of public- health measures in the community (eg, isolation of infected cases). Here, I discuss the mechanisms that generate symptoms associated with URTIs, especially common cold and flu, but will not review virology in any detail except as regards relevance to symptoms. Is it a cold or flu? The clinical expression of URTIs is variable and is partly influenced by the nature of the infecting virus but to a greater extent is modulated by the age, physiological state, and immunological experience of the host. 4 Depending on these factors, URTIs may occur without symptoms, may kill, or most commonly

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• num_train_epochs: 10
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 10
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: 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: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	34	0.9108
1.4706	50	0.9098
2.0	68	0.8834
2.9412	100	0.9051
3.0	102	0.9066
4.0	136	0.9205
4.4118	150	0.9019
5.0	170	0.9156
5.8824	200	0.9247
6.0	204	0.9238
7.0	238	0.9019
7.3529	250	0.8856
8.0	272	0.8856
8.8235	300	0.8879
9.0	306	0.8879
10.0	340	0.8865

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• 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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}