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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:499184
- loss:MultipleNegativesRankingLoss
base_model: jxm/cde-small-v2
widget:
- source_sentence: Heterozygous Advantage Definition
sentences:
- A heterozygote advantage (heterozygous advantage) describes the case in which
the heterozygote genotype has a higher relative fitness than either the homozygote
dominant or homozygote recessive genotype.
- Science Main Index. Animals with an internal skeleton made of bone are called
vertebrates. Vertebrates include fish, amphibians, reptiles, birds, mammals, primates,
rodents and marsupials. Although vertebrates represent only a very small percentage
of all animals, their size and mobility often allow them to dominate their environment.
- 'By Regina Bailey. Definition: Heterozygous refers to having two different alleles
for a single trait. Related Terms: Allele, Genes, Homozygous. Examples: The gene
for seed shape in pea plants exists in two forms, one form or allele for round
seed shape (R) and the other for wrinkled seed shape (r). heterozygous plant would
contain the following alleles for seed shape: (Rr). Organisms have two alleles
for each trait. When the alleles of a pair are heterozygous, one is dominant and
the other is recessive. Using the previous example, round seed shape (R) is dominant
and wrinkled seed shape (r) is recessive.'
- source_sentence: definition of annul
sentences:
- "When a celebrity wakes up in Las Vegas with a mysterious wedding ring on her\
\ finger, the first thing sheâ\x80\x99ll probably want to do is annul the marriage.\
\ That will declare it invalid and officially cancel the whole deal. Annul, which\
\ means â\x80\x9Cto cancelâ\x80\x9D or â\x80\x9Cto invalidate,â\x80\x9D is usually\
\ used in the context of politics or marriage. New government officials often\
\ want to annul laws and policies of the previous post-holder, effectively reversing\
\ their work. When you annul a marriage, you are officially declaring it invalid,\
\ as if it never happened."
- 'The proper term for Catholic annulment is declaration of nullity: the Church
declares that the marriage never was valid in the first place. This becomes clearer
when we compare Catholic annulment to civil divorce. A divorce is effective as
of the date of the divorce decree.Before that, the couple was still married.nnulment
for an invalid marriage Catholic annulment means that a couple was never married
in the sacramental sense. God did not create that unbreakable bond between them
because the sacrament of marriage was not actually fulfilled. The term annulment
is actually a little misleading.'
- Another word for consistent word list. Below are a number of words whose meaning
is similar to consistent. 1 accordant. 2 compatible. 3 conformable. 4 congruous.
5 harmonious. 6 suitable. 7 uniform.
- source_sentence: how much do peds nurse make
sentences:
- Vyvanse is detectable in urine up to 3 days after ingesting Vyvanse. Vyvanse is
detectable in hair samples for months after ingestion. Though Vyvanse itself only
stays in your system four hours post-ingestion, the active drug d-amphetamine
stays in your system for 40 hours.
- A newly practicing pediatric nurse in the US receives a beginning yearly salary
of around $31,311 but as he/she gains experience, he/she can anticipate a yearly
income of up to $81,840. The national hourly rate for Pediatric Nurse is from
between $15.53 to $35.81 with an average overtime pay of $6.93 to $54.59 per hour.
- 'Rad Tech Salary: $64,450 a year. Average pay for rad techs is $64,450 per annum,
which is 35% higher than the US median income. A radiographer makes an average
of $5,371 per month; $1,239 a week and $30.99 an hour. radiology technologist
can make more than $87,160 a year depending on many factors like work place, education,
experience, performance, etc. Working at schools ($74,810) or specialty hospitals
($72,410) would help you make more money than other industries. Massachusetts
is one of the best state based on annual income.'
- source_sentence: cost of six sigma certification
sentences:
- "The Roosevelt Corollary was an addition to the Monroe Doctrine which stated that\
\ no European countries were allowed to intervene with Latin American affairs.\
\ The only way that â\x80¦ the U.S was allowed to become involved was if the affairs\
\ or European countries was threatened."
- 1 The cost of the certification exams varies per training center, so you still
need to contact the center nearest you to get the actual price. 2 However, if
we look at the centers that have published their exam rates, we found that the
average cost of the exam is between $130 and $170. The costs of these training
programs could cost anywehre from $1,500 to more than $2,500. 2 For example,
a training course for AutoCAD being offered by Delta.edu costs $2,595.
- You can buy this ExpertRating Online Six Sigma Green Belt Certification. leading
to Certification at a special offer price of only $99.99 which includes the in-depth
ExpertRating Online Six Sigma Green Belt Courseware and exam fee. The ExpertRating
Six Sigma Green Belt Certification is by far the best value for money Six Sigma
Green Belt Certification at $99.99. Worldwide airmail delivery of the hard copy
Six Sigma Green Belt certificate. The certificate can be used to prove your certified
status and does not mention the word online.
- source_sentence: when did jeepers creepers come out
sentences:
- Jeepers Creepers Wiki. Creeper. Creeper is a fictional character and the main
antagonist in the 2001 horror film Jeepers Creepers and its 2003 sequel Jeepers
Creepers II. It is an ancient, mysterious demon who viciously feeds on the flesh
and bones of many human beings for 23 days every 23rd spring.
- Moline, IL,sales tax rate is 7.25%, and the Income tax is 8.92%.
- ' Creep is a song by the English alternative rock band Radiohead. Radiohead released
Creep as their debut single in 1992, and it later appeared on their first album,
Pablo Honey (1993). During its initial release, Creep was not a chart success.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on jxm/cde-small-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jxm/cde-small-v2](https://huggingface.co/jxm/cde-small-v2). It maps sentences & paragraphs to a None-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:** [jxm/cde-small-v2](https://huggingface.co/jxm/cde-small-v2) <!-- at revision 287bf0ea6ebfecf2339762d0ef28fb846959a8f2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** None 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({}) with Transformer model: ContextualDocumentEmbeddingTransformer
)
```
## 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("BlackBeenie/cde-small-v2-biencoder-msmarco")
# Run inference
sentences = [
'when did jeepers creepers come out',
'Jeepers Creepers Wiki. Creeper. Creeper is a fictional character and the main antagonist in the 2001 horror film Jeepers Creepers and its 2003 sequel Jeepers Creepers II. It is an ancient, mysterious demon who viciously feeds on the flesh and bones of many human beings for 23 days every 23rd spring.',
' Creep is a song by the English alternative rock band Radiohead. Radiohead released Creep as their debut single in 1992, and it later appeared on their first album, Pablo Honey (1993). During its initial release, Creep was not a chart success.',
]
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]
```
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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: 499,184 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.26 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 81.55 tokens</li><li>max: 203 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 80.95 tokens</li><li>max: 231 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what year did the sandy hook incident happen</code> | <code>For Newtown, 2012 Sandy Hook Elementary School shooting is still painful. It's been three years since the terrible day Jimmy Greeneâs 6-year-old daughter, Ana Grace Marquez, and 19 other children were murdered in the mass shooting at Sandy Hook Elementary School. But life without Ana, who loved to sing and dance from room to room, continues to be so hard that, in some ways, Dec. 14 is no tougher than any other day for Greene.</code> | <code>Hook is a 1991 Steven Spielberg film starring Dustin Hoffman and Robin Williams. The film's storyline is based on the books written by Sir James Matthew Barrie in 1904 or 1905 and is the sequel to the first book.</code> |
| <code>what kind of degree do you need to be a medical assistant?</code> | <code>If you choose this path, here is what you need to do: 1 Have a high school diploma or GED. The minimum educational requirement for medical assistants is a high school diploma or equivalency degree. 2 Find a doctor who will provide training.</code> | <code>Many colleges offer two-year associate's degrees or one-year certificate programs in different areas of medical office technology. Certificate areas include billing specialist, medical administrative assistant, and medical transcriptionist. Because of the complexity of medical jargon and operational procedures, many employers prefer these professionals to hold related two-year degrees or complete one-year training programs.</code> |
| <code>what does usb cord do</code> | <code>The Flash Player is required to see this video. The term USB stands for Universal Serial Bus. USB cable assemblies are some of the most popular cable types available, used mostly to connect computers to peripheral devices such as cameras, camcorders, printers, scanners, and more. Devices manufactured to the current USB Revision 3.0 specification are backward compatible with version 1.1.</code> | <code>The USB 2.0 specification for a Full-Speed/High-Speed cable calls for four wires, two for data and two for power, and a braided outer shield. The USB 3.0 specification calls for a total of 10 wires plus a braided outer shield. Two wires are used for power.</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
- `fp16`: True
- `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`: 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}
- `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
- `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
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0321 | 500 | 0.9856 |
| 0.0641 | 1000 | 0.4499 |
| 0.0962 | 1500 | 0.3673 |
| 0.1282 | 2000 | 0.339 |
| 0.1603 | 2500 | 0.3118 |
| 0.1923 | 3000 | 0.2929 |
| 0.2244 | 3500 | 0.2886 |
| 0.2564 | 4000 | 0.2771 |
| 0.2885 | 4500 | 0.2762 |
| 0.3205 | 5000 | 0.2716 |
| 0.3526 | 5500 | 0.2585 |
| 0.3846 | 6000 | 0.2631 |
| 0.4167 | 6500 | 0.2458 |
| 0.4487 | 7000 | 0.2496 |
| 0.4808 | 7500 | 0.252 |
| 0.5128 | 8000 | 0.2399 |
| 0.5449 | 8500 | 0.2422 |
| 0.5769 | 9000 | 0.2461 |
| 0.6090 | 9500 | 0.2314 |
| 0.6410 | 10000 | 0.2331 |
| 0.6731 | 10500 | 0.2314 |
| 0.7051 | 11000 | 0.2302 |
| 0.7372 | 11500 | 0.235 |
| 0.7692 | 12000 | 0.2176 |
| 0.8013 | 12500 | 0.2201 |
| 0.8333 | 13000 | 0.2206 |
| 0.8654 | 13500 | 0.222 |
| 0.8974 | 14000 | 0.2136 |
| 0.9295 | 14500 | 0.2108 |
| 0.9615 | 15000 | 0.2102 |
| 0.9936 | 15500 | 0.2098 |
| 1.0256 | 16000 | 0.1209 |
| 1.0577 | 16500 | 0.099 |
| 1.0897 | 17000 | 0.0944 |
| 1.1218 | 17500 | 0.0955 |
| 1.1538 | 18000 | 0.0947 |
| 1.1859 | 18500 | 0.0953 |
| 1.2179 | 19000 | 0.0943 |
| 1.25 | 19500 | 0.0911 |
| 1.2821 | 20000 | 0.0964 |
| 1.3141 | 20500 | 0.0933 |
| 1.3462 | 21000 | 0.0956 |
| 1.3782 | 21500 | 0.0941 |
| 1.4103 | 22000 | 0.0903 |
| 1.4423 | 22500 | 0.0889 |
| 1.4744 | 23000 | 0.0919 |
| 1.5064 | 23500 | 0.0917 |
| 1.5385 | 24000 | 0.0956 |
| 1.5705 | 24500 | 0.0903 |
| 1.6026 | 25000 | 0.0931 |
| 1.6346 | 25500 | 0.0931 |
| 1.6667 | 26000 | 0.089 |
| 1.6987 | 26500 | 0.0892 |
| 1.7308 | 27000 | 0.091 |
| 1.7628 | 27500 | 0.0892 |
| 1.7949 | 28000 | 0.0884 |
| 1.8269 | 28500 | 0.0889 |
| 1.8590 | 29000 | 0.0877 |
| 1.8910 | 29500 | 0.0866 |
| 1.9231 | 30000 | 0.0853 |
| 1.9551 | 30500 | 0.085 |
| 1.9872 | 31000 | 0.0867 |
| 2.0192 | 31500 | 0.055 |
| 2.0513 | 32000 | 0.0338 |
| 2.0833 | 32500 | 0.033 |
| 2.1154 | 33000 | 0.033 |
| 2.1474 | 33500 | 0.0317 |
| 2.1795 | 34000 | 0.0323 |
| 2.2115 | 34500 | 0.0322 |
| 2.2436 | 35000 | 0.0316 |
| 2.2756 | 35500 | 0.0314 |
| 2.3077 | 36000 | 0.0312 |
| 2.3397 | 36500 | 0.0324 |
| 2.3718 | 37000 | 0.0324 |
| 2.4038 | 37500 | 0.0328 |
| 2.4359 | 38000 | 0.0311 |
| 2.4679 | 38500 | 0.0312 |
| 2.5 | 39000 | 0.0312 |
| 2.5321 | 39500 | 0.0311 |
| 2.5641 | 40000 | 0.0315 |
| 2.5962 | 40500 | 0.0308 |
| 2.6282 | 41000 | 0.0308 |
| 2.6603 | 41500 | 0.0306 |
| 2.6923 | 42000 | 0.0313 |
| 2.7244 | 42500 | 0.0322 |
| 2.7564 | 43000 | 0.0315 |
| 2.7885 | 43500 | 0.0311 |
| 2.8205 | 44000 | 0.0321 |
| 2.8526 | 44500 | 0.0318 |
| 2.8846 | 45000 | 0.0305 |
| 2.9167 | 45500 | 0.031 |
| 2.9487 | 46000 | 0.032 |
| 2.9808 | 46500 | 0.0306 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.50.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- 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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