metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:28050
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: What helps an insectivorous plant attract and digest insects?
sentences:
- >-
This investigation examined the accuracy of several generalizable
anthropometric (ANTHRO) and bioelectrical impedance (BIA) regression
equations to estimate % body fat (%BF) in women with either upper body
(UB) or lower body (LB) fat distribution patterns.
- >-
Bacteria can also be chemotrophs. Chemosynthetic bacteria, or
chemotrophs , obtain energy by breaking down chemical compounds in their
environment. An example of one of these chemicals broken down by
bacteria is nitrogen-containing ammonia. These bacteria are important
because they help cycle nitrogen through the environment for other
living things to use. Nitrogen cannot be made by living organisms, so it
must be continually recycled. Organisms need nitrogen to make organic
compounds, such as DNA.
- >-
Insectivorous Plants An insectivorous plant has specialized leaves to
attract and digest insects. The Venus flytrap is popularly known for its
insectivorous mode of nutrition, and has leaves that work as traps
(Figure 31.16). The minerals it obtains from prey compensate for those
lacking in the boggy (low pH) soil of its native North Carolina coastal
plains. There are three sensitive hairs in the center of each half of
each leaf. The edges of each leaf are covered with long spines. Nectar
secreted by the plant attracts flies to the leaf. When a fly touches the
sensory hairs, the leaf immediately closes. Next, fluids and enzymes
break down the prey and minerals are absorbed by the leaf. Since this
plant is popular in the horticultural trade, it is threatened in its
original habitat.
- source_sentence: >-
When carbon atoms are not bonded to as many hydrogen atoms as possible,
what kind of hydrocarbon results?
sentences:
- >-
Unsaturated hydrocarbons have at least one double or triple bond between
carbon atoms, so the carbon atoms are not bonded to as many hydrogen
atoms as possible. In other words, they are unsaturated with hydrogen
atoms.
- >-
Endoscopic radiofrequency ablation (RFA) is a promising new treatment of
Barrett's esophagus (BE). Adjunctive intra-esophageal pH control with
proton pump inhibitors and/or anti-reflux surgery is generally
recommended to optimize squamous re-epithelialization after ablation.
- >-
The cell wall is located outside the cell membrane. It consists mainly
of cellulose and may also contain lignin, which makes it more rigid. The
cell wall shapes, supports, and protects the cell. It prevents the cell
from absorbing too much water and bursting. It also keeps large,
damaging molecules out of the cell.
- source_sentence: >-
Do comparison of ambulance dispatch protocols for nontraumatic abdominal
pain?
sentences:
- >-
KIOM-79, a combination of four plant extracts, has a preventive effect
on diabetic nephropathy and retinopathy in diabetic animal models. In
this study, we have investigated the inhibitory effects of KIOM-79 on
diabetic cataractogenesis.
- >-
To compare rates of undertriage and overtriage of six ambulance dispatch
protocols for the presenting complaint of nontraumatic abdominal pain,
and to identify the optimal protocol.
- a flower is a source of nectar
- source_sentence: >-
Does altered fractalkine cleavage potentially promote local inflammation
in NOD salivary gland?
sentences:
- >-
In France, when physicians in ambulances take care of patients, they
report medical status to the dispatch centre. Then the dispatching
physician search for the available and appropriate hospital service to
agree in directly receiving the patient. We attempted to evaluate this
direct admission dispatch, in a urban area, with many health care
facilities.
- >-
Despite the high prevalence of cannabis use in schizophrenia, few
studies have examined the potential relationship between cannabis
exposure and brain structural abnormalities in schizophrenia.
- >-
In the nonobese diabetic (NOD) mouse model of Sjögren's syndrome,
lymphocytic infiltration is preceded by an accumulation of dendritic
cells in the submandibular glands (SMGs). NOD mice also exhibit an
increased frequency of mature, fractalkine receptor (CX3C chemokine
receptor [CX3CR]1) expressing monocytes, which are considered to be
precursors for tissue dendritic cells. To unravel further the role
played by fractalkine-CX3CR1 interactions in the salivary gland
inflammation, we studied the expression of fractalkine in NOD SMGs.
- source_sentence: The smallest cyclic ether is called what?
sentences:
- >-
Most human traits have more complex modes of inheritance than simple
Mendelian inheritance. For example, the traits may be controlled by
multiple alleles or multiple genes.
- >-
Neonatal stress impairs postnatal bone mineralization. Evidence suggests
that mechanical tactile stimulation (MTS) in early life decreases stress
hormones and improves bone mineralization. Insulin-like growth factor
(IGF1) is impacted by stress and essential to bone development. We
hypothesized that MTS administered during neonatal stress would improve
bone phenotype in later life. We also predicted an increase in bone
specific mRNA expression of IGF1 related pathways.
- The smallest cyclic ether is called an epoxide. Draw its structure.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-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-base-en-v1.5
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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("danthepol/mcqa_embedder_v2")
# Run inference
sentences = [
'The smallest cyclic ether is called what?',
'The smallest cyclic ether is called an epoxide. Draw its structure.',
'Neonatal stress impairs postnatal bone mineralization. Evidence suggests that mechanical tactile stimulation (MTS) in early life decreases stress hormones and improves bone mineralization. Insulin-like growth factor (IGF1) is impacted by stress and essential to bone development. We hypothesized that MTS administered during neonatal stress would improve bone phenotype in later life. We also predicted an increase in bone specific mRNA expression of IGF1 related pathways.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Training Dataset
Unnamed Dataset
- Size: 28,050 training samples
- Columns:
sentence_0
andsentence_1
- Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 type string string details - min: 6 tokens
- mean: 23.02 tokens
- max: 63 tokens
- min: 5 tokens
- mean: 81.53 tokens
- max: 512 tokens
- Samples:
sentence_0 sentence_1 Ectotherms undergo a variety of changes at the cellular level to acclimatize to shifts in what?
There are 44 autosomes and 2 sex chromosomes in the human genome, for a total of 46 chromosomes (23 pairs). Sex chromosomes specify an organism's genetic sex. Humans can have two different sex chromosomes, one called X and the other Y. Normal females possess two X chromosomes and normal males one X and one Y. An autosome is any chromosome other than a sex chromosome. The Figure below shows a representation of the 24 different human chromosomes. Figure below shows a karyotype of the human genome. A karyotype depicts, usually in a photograph, the chromosomal complement of an individual, including the number of chromosomes and any large chromosomal abnormalities. Karyotypes use chromosomes from the metaphase stage of mitosis.
All polar compounds contain what type of bonds?
Polar compounds, such as water, are compounds that have a partial negative charge on one side of each molecule and a partial positive charge on the other side. All polar compounds contain polar bonds (although not all compounds that contain polar bonds are polar. ) In a polar bond, two atoms share electrons unequally. One atom attracts the shared electrons more strongly, so it has a partial negative charge. The other atom attracts the shared electrons less strongly, so it is has a partial positive charge. In a water molecule, the oxygen atom attracts the shared electrons more strongly than the hydrogen atoms do. This explains why the oxygen side of the water molecule has a partial negative charge and the hydrogen side of the molecule has a partial positive charge.
Do lateral cephalometric radiograph for the planning of maxillary implant reconstruction?
To present a simple and objective method for the planning of maxillary implant reconstruction with autogenous bone graft in maxilla atrophy.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
per_device_train_batch_size
: 32per_device_eval_batch_size
: 32multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 3max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}tp_size
: 0fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | Training Loss |
---|---|---|
0.5701 | 500 | 0.064 |
1.1403 | 1000 | 0.0455 |
1.7104 | 1500 | 0.0254 |
2.2805 | 2000 | 0.0189 |
2.8506 | 2500 | 0.0155 |
Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.3.0+cu121
- Accelerate: 1.3.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
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}
}