---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:53851
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: A certain junior class has 1000 students and a certain senior class
has 900 students. Among these students, there are 60 siblings pairs each consisting
of 1 junior and 1 senior. If 1 student is to be selected at random from each class,
what is the probability that the 2 students selected will be a sibling pair?
sentences:
- Let's see Pick 60/1000 first Then we can only pick 1 other pair from the 800 So
total will be 60 / 900 *1000 Simplify and you get 2/30000
- To maximize number of hot dogs with 300$ Total number of hot dogs bought in 250-pack
= 22.95*13 =298.35$ Amount remaining = 300 - 298.35 = 1.65$ This amount is too
less to buy any 8- pack . Greatest number of hot dogs one can buy with 300 $ =
250*13 = 3250
- artificial leg
- source_sentence: A stock trader originally bought 300 shares of stock from a company
at a total cost of m dollars. If each share was sold at 80% above the original
cost per share of stock, then interns of m for how many dollars was each share
sold?
sentences:
- Let Cost of 300 shares be $ 3000 So, Cost of 1 shares be $ 10 =>m/300 Selling
price per share = (100+80)/100 * m/300 Or, Selling price per share = 9/5 * m/300
=> 9m/1500
- The prognostic value of p53 nuclear accumulation in gastric cancer is still unclear,
as shown by the discordant results still reported in the literature. In this study,
we evaluated the correlation between p53 accumulation and long-term survival of
patients resected for intestinal and diffuse-type gastric cancer. Eighty-three
patients with carcinoma of the intestinal type and 53 patients with carcinoma
of the diffuse type were included in the study. Immunohistochemical staining of
the paraffin sections was performed by using monoclonal antibody DO1; cases were
considered positive when nuclear immunostaining was observed in 10% or more of
the tumor cells. Prognostic significance of different variables was investigated
by univariate and multivariate analysis. p53 positivity was found in 51.8% of
intestinal-type and 50.9% of diffuse-type cases. No significant correlation between
the rate of p53 overexpression and age, sex, tumor location, tumor size, depth
of invasion, lymph node involvement, distant metastases, and surgical radicality
was found in the two groups of patients. A statistically significant difference
in survival rate was observed between p53-negative and p53-positive cases in the
intestinal type (P < .05), confirmed by multivariate analysis (P < .005; relative
risk = 3.09). On the contrary, no correlation with survival was found in diffuse-type
cases according to p53 overexpression.
- Many animal behaviors occur in a regular cycle. Two types of cyclic behaviors
are circadian rhythms and migration.
- source_sentence: Are lactate levels in severe malarial anaemia associated with haemozoin-containing
neutrophils and low levels of IL-12?
sentences:
- Hyperlactataemia is often associated with a poor outcome in severe malaria in
African children. To unravel the complex pathophysiology of this condition the
relationship between plasma lactate levels, parasite density, pro- and anti-inflammatory
cytokines, and haemozoin-containing leucocytes was studied in children with severe
falciparum malarial anaemia. Twenty-six children with a primary diagnosis of severe
malarial anaemia with any asexual Plasmodium falciparum parasite density and Hb
< 5 g/dL were studied and the association of plasma lactate levels and haemozoin-containing
leucocytes, parasite density, pro- and anti-inflammatory cytokines was measured.
The same associations were measured in non-severe malaria controls (N = 60). Parasite
density was associated with lactate levels on admission (r = 0.56, P < 0.005).
Moreover, haemozoin-containing neutrophils and IL-12 were strongly associated
with plasma lactate levels, independently of parasite density (r = 0.60, P = 0.003
and r = -0.46, P = 0.02, respectively). These associations were not found in controls
with uncomplicated malarial anaemia.
- one of two female reproductive organs that produces eggs and secretes estrogen.
- hydrogen
- source_sentence: Does phosphatidylethanol mediate its effects on the vascular endothelial
growth factor via HDL receptor in endothelial cells?
sentences:
- 'Patients having previous bariatric surgery are at risk for weight regain and
return of co-morbidities. If an anatomic basis for the failure is identified,
many surgeons advocate revision or conversion to a Roux-en-Y gastric bypass. The
aim of this study was to determine whether revisional bariatric surgery leads
to sufficient weight loss and co-morbidity remission. From 2005-2012, patients
undergoing revision were entered into a prospectively maintained database. Perioperative
outcomes, including complications, weight loss, and co-morbidity remission, were
examined for all patients with a history of a previous vertical banded gastroplasty
(VBG) or Roux-en-Y gastric bypass (RYGB). Twenty-two patients with a history of
RYGB and 56 with a history of VBG were identified. Following the revisional procedure,
the RYGB group experienced 35.8% excess weight loss (%EWL) and a 31.8% morbidity
rate. For the VBG group, patients experienced a 46.2% %EWL from their weight before
the revisional operation with a 51.8% morbidity rate. Co-morbidity remission rate
was excellent. Diabetes (VBG:100%, RYGB: 85.7%), gastroesophageal reflux disease
(VBG: 94.4%, RYGB: 80%), and hypertension (VBG: 74.2%, RYGB:60%) demonstrated
significant improvement.'
- 'Explanation: Let A, B, C represent their respective weights. Then, we have: A
+ B + C = (45 x 3) = 135 …. (i) A + B = (40 x 2) = 80 …. (ii) B + C = (44 x 2)
= 88 ….(iii) Adding (ii) and (iii), we get: A + 2B + C = 168 …. (iv) Subtracting
(i) from (iv), we get : B = 33. B’s weight = 33 kg.'
- Previous epidemiological studies have shown that light to moderate alcohol consumption
has protective effects against coronary heart disease but the mechanisms of the
beneficial effect of alcohol are not known. Ethanol may increase high density
lipoprotein (HDL) cholesterol concentration, augment the reverse cholesterol transport,
or regulate growth factors or adhesion molecules. To study whether qualitative
changes in HDL phospholipids mediate part of the beneficial effects of alcohol
on atherosclerosis by HDL receptor, we investigated whether phosphatidylethanol
(PEth) in HDL particles affects the secretion of vascular endothelial growth factor
(VEGF) by a human scavenger receptor CD36 and LIMPII analog-I (CLA-1)-mediated
pathway. Human EA.hy 926 endothelial cells were incubated in the presence of native
HDL or PEth-HDL. VEGF concentration and CLA-1 protein expression were measured.
Human CLA-1 receptor-mediated mechanisms in endothelial cells were studied using
CLA-1 blocking antibody and protein kinase inhibitors. Phosphatidylethanol-containing
HDL particles caused a 6-fold increase in the expression of CLA-1 in endothelial
cells compared with the effect of native HDL. That emergent effect was mediated
mainly through protein kinase C and p44/42 mitogen-activated protein kinase pathways.
PEth increased the secretion of VEGF and that increase could be abolished by a
CLA-1 blocking antibody.
- source_sentence: Said to go hand-in-hand with science, what evolves as new materials,
designs, and processes are invented?
sentences:
- Technology evolves as new materials, designs, and processes are invented.
- Technological design constraints may be physical or social.
- let x=44444444,then 44444445=x+1 88888885=2x-3 44444442=x-2 44444438=x-6 44444444^2=x^2
then substitute it in equation (x+1)(2x-3)(x-2)+(x-6)/x^2 ans is 2x-5 i.e 88888883
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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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/MNLP_M3_document_encoder")
# Run inference
sentences = [
'Said to go hand-in-hand with science, what evolves as new materials, designs, and processes are invented?',
'Technology evolves as new materials, designs, and processes are invented.',
'Technological design constraints may be physical or social.',
]
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: 53,851 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 31.16 tokens
- max: 143 tokens
| - min: 3 tokens
- mean: 160.39 tokens
- max: 512 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| For integers U and V, when U is divided by V, the remainder is odd. Which of the following must be true? | At least one of U and V is odd |
| A mailman puts .05% of letters in the wrong mailbox. How many deliveries must he make to misdeliver 2 items? | Let the number of total deliveries be x Then, .05% of x=2 (5/100)*(1/100)*x=2 x=4000 |
| A certain ball team has an equal number of right- and left-handed players. On a certain day, two-thirds of the players were absent from practice. Of the players at practice that day, two-third were left handed. What is the ratio of the number of right-handed players who were not at practice that day to the number of lefthanded players who were not at practice? | Say the total number of players is 18, 9 right-handed and 9 left-handed. On a certain day, two-thirds of the players were absent from practice --> 12 absent and 6 present. Of the players at practice that day, one-third were left-handed --> 6*2/3=4 were left-handed and 2 right-handed. The number of right-handed players who were not at practice that day is 9-2=7. The number of left-handed players who were not at practice that days is 9-4=5. The ratio = 7/5. |
* Loss: [MultipleNegativesRankingLoss](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
Click to expand
- `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