metadata

base_model: BAAI/bge-small-en-v1.5
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:4012
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Extensive messenger RNA editing generates transcript and protein diversity
      in genes involved in neural excitability, as previously described, as well
      as in genes participating in a broad range of other cellular functions. 
    sentences:
      - Do cephalopods use RNA editing less frequently than other species?
      - GV1001 vaccine targets which enzyme?
      - Which event results in the acetylation of S6K1?
  - source_sentence: >-
      Yes, exposure to household furry pets influences the gut microbiota of
      infants.
    sentences:
      - Can pets affect infant microbiomed?
      - What is the mode of action of Thiazovivin?
      - What are the effects of CAMK4 inhibition?
  - source_sentence: >-
      In children with heart failure evidence of the effect of enalapril is
      empirical. Enalapril was clinically safe and effective in 50% to 80% of
      for children with cardiac failure secondary to congenital heart
      malformations before and after cardiac surgery,  impaired ventricular
      function , valvar regurgitation,  congestive cardiomyopathy,  , arterial
      hypertension, life-threatening arrhythmias coexisting with circulatory
      insufficiency.   

      ACE inhibitors have shown a transient beneficial effect on heart failure
      due to anticancer drugs and possibly a beneficial effect in muscular
      dystrophy-associated cardiomyopathy, which deserves further studies.
    sentences:
      - Which receptors can be evaluated with the [18F]altanserin?
      - >-
        In what proportion of children with heart failure has Enalapril been
        shown to be safe and effective?
      - Which major signaling pathways are regulated by RIP1?
  - source_sentence: >-
      Cellular senescence-associated heterochromatic foci (SAHFS) are a novel
      type of chromatin condensation involving alterations of linker histone H1
      and linker DNA-binding proteins. SAHFS can be formed by a variety of cell
      types, but their mechanism of action remains unclear.
    sentences:
      - >-
        What is the relationship between the X chromosome and a  neutrophil
        drumstick?
      - Which microRNAs are involved in exercise adaptation?
      - How are SAHFS created?
  - source_sentence: >-
      Multicluster Pcdh diversity is required for mouse olfactory neural circuit
      assembly. The vertebrate clustered protocadherin (Pcdh) cell surface
      proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ,
      and Pcdhγ). Although deletion of individual Pcdh clusters had subtle
      phenotypic consequences, the loss of all three clusters (tricluster
      deletion) led to a severe axonal arborization defect and loss of
      self-avoidance.
    sentences:
      - >-
        What are the effects of the deletion of all three Pcdh clusters
        (tricluster deletion) in mice?
      - what is the role of MEF-2 in cardiomyocyte differentiation?
      - >-
        How many periods of regulatory innovation led to the evolution of
        vertebrates?
model-index:
  - name: BGE small finetuned BIOASQ
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: BAAI/bge small en v1.5
          type: BAAI/bge-small-en-v1.5
        metrics:
          - type: cosine_accuracy@1
            value: 0.8415841584158416
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.925035360678925
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.942008486562942
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.958981612446959
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8415841584158416
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.30834512022630833
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18840169731258838
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09589816124469587
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8415841584158416
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.925035360678925
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.942008486562942
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.958981612446959
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9047357964584107
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.886919916481444
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8877807671526188
            name: Cosine Map@100

BGE small finetuned BIOASQ

This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. It maps sentences & paragraphs to a 384-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: BAAI/bge-small-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("juanpablomesa/bge-small-bioasq-1epochs-batch32")
# Run inference
sentences = [
    'Multicluster Pcdh diversity is required for mouse olfactory neural circuit assembly. The vertebrate clustered protocadherin (Pcdh) cell surface proteins are encoded by three closely linked gene clusters (Pcdhα, Pcdhβ, and Pcdhγ). Although deletion of individual Pcdh clusters had subtle phenotypic consequences, the loss of all three clusters (tricluster deletion) led to a severe axonal arborization defect and loss of self-avoidance.',
    'What are the effects of the deletion of all three Pcdh clusters (tricluster deletion) in mice?',
    'How many periods of regulatory innovation led to the evolution of vertebrates?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: BAAI/bge-small-en-v1.5
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8416
cosine_accuracy@3	0.925
cosine_accuracy@5	0.942
cosine_accuracy@10	0.959
cosine_precision@1	0.8416
cosine_precision@3	0.3083
cosine_precision@5	0.1884
cosine_precision@10	0.0959
cosine_recall@1	0.8416
cosine_recall@3	0.925
cosine_recall@5	0.942
cosine_recall@10	0.959
cosine_ndcg@10	0.9047
cosine_mrr@10	0.8869
cosine_map@100	0.8878

Training Details

Training Dataset

Unnamed Dataset

Size: 4,012 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 3 tokens
mean: 63.38 tokens
max: 485 tokens

min: 5 tokens
mean: 16.13 tokens
max: 49 tokens

	positive	anchor
type	string	string
details	min: 3 tokens mean: 63.38 tokens max: 485 tokens	min: 5 tokens mean: 16.13 tokens max: 49 tokens

Samples:

positive	anchor
`Aberrant patterns of H3K4, H3K9, and H3K27 histone lysine methylation were shown to result in histone code alterations, which induce changes in gene expression, and affect the proliferation rate of cells in medulloblastoma.`	`What is the implication of histone lysine methylation in medulloblastoma?`
`STAG1/STAG2 proteins are tumour suppressor proteins that suppress cell proliferation and are essential for differentiation.`	`What is the role of STAG1/STAG2 proteins in differentiation?`
`The association between cell phone use and incident glioblastoma remains unclear. Some studies have reported that cell phone use was associated with incident glioblastoma, and with reduced survival of patients diagnosed with glioblastoma. However, other studies have repeatedly replicated to find an association between cell phone use and glioblastoma.`	`What is the association between cell phone use and glioblastoma?`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

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: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: True
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	BAAI/bge-small-en-v1.5_cosine_map@100
0.0794	10	0.5344	-
0.1587	20	0.4615	-
0.2381	30	0.301	-
0.3175	40	0.2169	-
0.3968	50	0.1053	-
0.4762	60	0.1432	-
0.5556	70	0.1589	-
0.6349	80	0.1458	-
0.7143	90	0.1692	-
0.7937	100	0.1664	-
0.8730	110	0.1252	-
0.9524	120	0.1243	-
1.0	126	-	0.8858
0.0794	10	0.1393	-
0.1587	20	0.1504	-
0.2381	30	0.1009	-
0.3175	40	0.0689	-
0.3968	50	0.0301	-
0.4762	60	0.0647	-
0.5556	70	0.0748	-
0.6349	80	0.0679	-
0.7143	90	0.1091	-
0.7937	100	0.0953	-
0.8730	110	0.089	-
0.9524	120	0.0758	-
1.0	126	-	0.8878
0.0794	10	0.092	-
0.1587	20	0.0748	-
0.2381	30	0.0392	-
0.3175	40	0.014	-
0.3968	50	0.0057	-
0.4762	60	0.0208	-
0.5556	70	0.0173	-
0.6349	80	0.0195	-
0.7143	90	0.0349	-
0.7937	100	0.0483	-
0.8730	110	0.0254	-
0.9524	120	0.0325	-
1.0	126	-	0.8883
1.0317	130	0.0582	-
1.1111	140	0.0475	-
1.1905	150	0.0325	-
1.2698	160	0.0058	-
1.3492	170	0.0054	-
1.4286	180	0.0047	-
1.5079	190	0.0076	-
1.5873	200	0.0091	-
1.6667	210	0.0232	-
1.7460	220	0.0147	-
1.8254	230	0.0194	-
1.9048	240	0.0186	-
1.9841	250	0.0141	-
2.0	252	-	0.8857
2.0635	260	0.037	-
2.1429	270	0.0401	-
2.2222	280	0.0222	-
2.3016	290	0.0134	-
2.3810	300	0.008	-
2.4603	310	0.0199	-
2.5397	320	0.017	-
2.6190	330	0.0164	-
2.6984	340	0.0344	-
2.7778	350	0.0352	-
2.8571	360	0.0346	-
2.9365	370	0.0256	-
3.0	378	-	0.8868
0.7937	100	0.0064	0.8878

Framework Versions

Python: 3.11.5
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.31.0
Datasets: 2.19.1
Tokenizers: 0.19.1

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",
}

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}
}