Gonalb/flucold-ft-v1

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-v1")
# Run inference
sentences = [
    'QUESTION #2: How does the sneeze centre in the brainstem coordinate the actions involved in sneezing?',
    'causes sneezing.23 The trigeminal nerves relay\ninformation to the sneeze centre in the brainstem and\ncause reflex activation of motor and parasympathetic\nbranches of the facial nerve and activate respiratory\nmuscles. A model of the sneeze reflex is illustrated in\nfigure 1. The sneeze centre coordinates the inspiratory\nand expiratory actions of sneezing via respiratory\nmuscles, and lacrimation and nasal congestion via\nparasympathetic branches of the facial nerve. The eyes\nare always closed during sneezing by activation of facial\nmuscles, indicating a close relation between the',
    'stroke, seizure disorder, dementia)\nAsthma or other chronic pulmonary disease\nChronic kidney disease\nChronic liver disease\nHeart disease (acquired or congenital)\nImmunosuppression (e.g., HIV infection, cancer, transplant \nrecipients, use of immunosuppressive medications)\nLong-term aspirin therapy in patients younger than 19 years\nMetabolic disorders (acquired [e.g., diabetes mellitus] or \ninherited [e.g., mitochondrial disorders])\nMorbid obesity\nSickle cell anemia and other hemoglobinopathies\nSpecial groups\nAdults 65 years and older\nAmerican Indians and Alaska Natives',
]
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	0.6122
cosine_accuracy@3	0.8878
cosine_accuracy@5	0.9388
cosine_accuracy@10	0.9898
cosine_precision@1	0.6122
cosine_precision@3	0.2959
cosine_precision@5	0.1878
cosine_precision@10	0.099
cosine_recall@1	0.6122
cosine_recall@3	0.8878
cosine_recall@5	0.9388
cosine_recall@10	0.9898
cosine_ndcg@10	0.8165
cosine_mrr@10	0.7593
cosine_map@100	0.76

Training Details

Training Dataset

Unnamed Dataset

• Size: 400 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 400 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 14 tokens mean: 24.87 tokens max: 53 tokens	min: 44 tokens mean: 129.25 tokens max: 188 tokens

• Samples:

  sentence_0	sentence_1
  What is the recommended age for annual influenza vaccination according to the context?	recommend annual influenza vaccination for all people six months and older who do not have contraindications. 15,16 Vaccination efforts should target people at increased risk of complicated or severe influenza (Table 117-19) and those who care for or live with high-risk individuals, including health care professionals. 15 Two previous FPM articles provided communication strategies and tools for increasing influenza vaccination rates in practice. 20,21 Multiple formulations of the influenza vaccine are avail - able, including inactivated influenza vaccines (IIV); a recom-
  Who should vaccination efforts specifically target to prevent complicated or severe influenza?	recommend annual influenza vaccination for all people six months and older who do not have contraindications. 15,16 Vaccination efforts should target people at increased risk of complicated or severe influenza (Table 117-19) and those who care for or live with high-risk individuals, including health care professionals. 15 Two previous FPM articles provided communication strategies and tools for increasing influenza vaccination rates in practice. 20,21 Multiple formulations of the influenza vaccine are avail - able, including inactivated influenza vaccines (IIV); a recom-
  What types of studies were included in the search regarding influenza complications and treatment?	enza complications American Indians, influenza treatment, and influenza universal vaccine. The search included meta-analyses, randomized controlled trials, clinical trials, and reviews. Search dates: December 1, 2018, to October 5, 2019. The Authors DAVID Y. GAITONDE, MD, is a core clinical faculty member and chief of endocrinology service at Dwight D. Eisenhower Army Medical Center, Fort Gordon, Ga. CPT. FAITH C. MOORE, USA, MC, is a resident in the Depart - ment of Internal Medicine at Dwight D. Eisenhower Army Medical Center. MAJ. MACKENZIE K. MORGAN, USA, MC, is chief of infec-

• 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	40	0.8359
1.25	50	0.8312
2.0	80	0.8304
2.5	100	0.8156
3.0	120	0.8016
3.75	150	0.7952
4.0	160	0.7880
5.0	200	0.8021
6.0	240	0.8215
6.25	250	0.8286
7.0	280	0.8079
7.5	300	0.8043
8.0	320	0.8126
8.75	350	0.8099
9.0	360	0.8126
10.0	400	0.8165

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}
}