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---
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](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/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](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
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]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 28,050 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 23.02 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 81.53 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Ectotherms undergo a variety of changes at the cellular level to acclimatize to shifts in what?</code> | <code>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.</code> |
| <code>All polar compounds contain what type of bonds?</code> | <code>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.</code> |
| <code>Do lateral cephalometric radiograph for the planning of maxillary implant reconstruction?</code> | <code>To present a simple and objective method for the planning of maxillary implant reconstruction with autogenous bone graft in maxilla atrophy.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `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`: 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`: 3
- `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}
- `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`: round_robin
</details>
### 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
```bibtex
@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
```bibtex
@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}
}
```
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