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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100231
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: 'Represent this sentence for searching relevant passages: where
do the chances live on raising hope'
sentences:
- Raising Hope James "Jimmy" Chance is a 23-year old, living in the surreal fictional
town of Natesville, who impregnates a serial killer during a one-night stand.
Earning custody of his daughter, Hope, after the mother is sentenced to death,
Jimmy relies on his oddball but well-intentioned family for support in raising
the child.
- Quadripoint A quadripoint is a point on the Earth that touches the border of four
distinct territories.[1][2] The term has never been in common use—it may not have
been used before 1964 when it was possibly invented by the Office of the Geographer
of the United States Department of State.[3][n 1] The word does not appear in
the Oxford English Dictionary or Merriam-Webster Online dictionary, but it does
appear in the Encyclopædia Britannica,[4] as well as in the World Factbook articles
on Botswana, Namibia, Zambia, and Zimbabwe, dating as far back as 1990.[5]
- Show Me the Way to Go Home The song was recorded by several artists in the 1920s,
including radio personalities The Happiness Boys,[2] Vincent Lopez and his Orchestra,[2]
and the California Ramblers.[3] Throughout the twentieth into the twenty-first
century it has been recorded by numerous artists.
- source_sentence: 'Represent this sentence for searching relevant passages: who wrote
the book of john in the bible'
sentences:
- Gospel of John Although the Gospel of John is anonymous,[1] Christian tradition
historically has attributed it to John the Apostle, son of Zebedee and one of
Jesus' Twelve Apostles. The gospel is so closely related in style and content
to the three surviving Johannine epistles that commentators treat the four books,[2]
along with the Book of Revelation, as a single corpus of Johannine literature,
albeit not necessarily written by the same author.[Notes 1]
- Levi Strauss & Co. Levi Strauss & Co. /ˌliːvaɪ ˈstraʊs/ is a privately held[5]
American clothing company known worldwide for its Levi's /ˌliːvaɪz/ brand of denim
jeans. It was founded in May 1853[6] when German immigrant Levi Strauss came from
Buttenheim, Bavaria, to San Francisco, California to open a west coast branch
of his brothers' New York dry goods business.[7] The company's corporate headquarters
is located in the Levi's Plaza in San Francisco.[8]
- Saturday Night Fever Tony's friends come to the car along with an intoxicated
Annette. Joey says she has agreed to have sex with everyone. Tony tries to lead
her away, but is subdued by Double J and Joey, and sullenly leaves with the group
in the car. Double J and Joey rape Annette. Bobby C. pulls the car over on the
Verrazano-Narrows Bridge for their usual cable-climbing antics. Instead of abstaining
as usual, Bobby performs stunts more recklessly than the rest of the gang. Realizing
that he is acting recklessly, Tony tries to get him to come down. Bobby's strong
sense of despair, the situation with Pauline, and Tony's broken promise to call
him earlier that day all lead to a suicidal tirade about Tony's lack of caring
before Bobby slips and falls to his death in the water below.
- source_sentence: 'Represent this sentence for searching relevant passages: what
type of habitat do sea turtles live in'
sentences:
- Turbidity Governments have set standards on the allowable turbidity in drinking
water. In the United States, systems that use conventional or direct filtration
methods turbidity cannot be higher than 1.0 nephelometric turbidity units (NTU)
at the plant outlet and all samples for turbidity must be less than or equal to
0.3 NTU for at least 95 percent of the samples in any month. Systems that use
filtration other than the conventional or direct filtration must follow state
limits, which must include turbidity at no time exceeding 5 NTU. Many drinking
water utilities strive to achieve levels as low as 0.1 NTU.[11] The European standards
for turbidity state that it must be no more than 4 NTU.[12] The World Health Organization,
establishes that the turbidity of drinking water should not be more than 5 NTU,
and should ideally be below 1 NTU.[13]
- 'List of 1924 Winter Olympics medal winners Finnish speed skater Clas Thunberg
topped the medal count with five medals: three golds, one silver, and one bronze.
One of his competitors, Roald Larsen of Norway, also won five medals, with two
silver and three bronze medal-winning performances.[3] The first gold medalist
at these Games—and therefore the first gold medalist in Winter Olympic history—was
American speed skater Charles Jewtraw. Only one medal change took place after
the Games: in the ski jump competition, a marking error deprived American athlete
Anders Haugen of a bronze medal. Haugen pursued an appeal to the IOC many years
after the fact; he was awarded the medal after a 1974 decision in his favor.[1]'
- Sea turtle Sea turtles are generally found in the waters over continental shelves.
During the first three to five years of life, sea turtles spend most of their
time in the pelagic zone floating in seaweed mats. Green sea turtles in particular
are often found in Sargassum mats, in which they find shelter and food.[14] Once
the sea turtle has reached adulthood it moves closer to the shore.[15] Females
will come ashore to lay their eggs on sandy beaches during the nesting season.[16]
- source_sentence: 'Represent this sentence for searching relevant passages: what
triggers the release of calcium from the sarcoplasmic reticulum'
sentences:
- Pretty Little Liars (season 7) The season consisted of 20 episodes, in which ten
episodes aired in the summer of 2016, with the remaining ten episodes aired from
April 2017.[2][3][4] The season's premiere aired on June 21, 2016, on Freeform.[5]
Production and filming began in the end of March 2016, which was confirmed by
showrunner I. Marlene King.[6] The season premiere was written by I. Marlene King
and directed by Ron Lagomarsino.[7] King revealed the title of the premiere on
Twitter on March 17, 2016.[8] On August 29, 2016, it was confirmed that this would
be the final season of the series.[9]
- Wentworth (TV series) A seventh season was commissioned in April 2018, before
the sixth-season premiere, with filming commencing the following week and a premiere
set for 2019.
- Sarcoplasmic reticulum Calcium ion release from the SR, occurs in the junctional
SR/terminal cisternae through a ryanodine receptor (RyR) and is known as a calcium
spark.[10] There are three types of ryanodine receptor, RyR1 (in skeletal muscle),
RyR2 (in cardiac muscle) and RyR3 (in the brain).[11] Calcium release through
ryanodine receptors in the SR is triggered differently in different muscles. In
cardiac and smooth muscle an electrical impulse (action potential) triggers calcium
ions to enter the cell through an L-type calcium channel located in the cell membrane
(smooth muscle) or T-tubule membrane (cardiac muscle). These calcium ions bind
to and activate the RyR, producing a larger increase in intracellular calcium.
In skeletal muscle, however, the L-type calcium channel is bound to the RyR. Therefore
activation of the L-type calcium channel, via an action potential, activates the
RyR directly, causing calcium release (see calcium sparks for more details).[12]
Also, caffeine (found in coffee) can bind to and stimulate RyR. Caffeine works
by making the RyR more sensitive to either the action potential (skeletal muscle)
or calcium (cardiac or smooth muscle) therefore producing calcium sparks more
often (this can result in increased heart rate, which is why we feel more awake
after coffee).[13]
- source_sentence: 'Represent this sentence for searching relevant passages: what
topic do all scientific questions have in common'
sentences:
- 'Jane Wyatt Wyatt portrayed Amanda Grayson, Spock''s mother and Ambassador Sarek''s
(Mark Lenard) wife, in the 1967 episode "Journey to Babel" of the original NBC
series, Star Trek, and the 1986 film Star Trek IV: The Voyage Home.[9] Wyatt was
once quoted as saying her fan mail for these two appearances in this role exceeded
that of Lost Horizon. In 1969, she made a guest appearance on Here Come the Brides,
but did not have any scenes with Mark Lenard, who was starring on the show as
sawmill owner Aaron Stemple.'
- Minnesota Vikings The Vikings played in Super Bowl XI, their third Super Bowl
(fourth overall) in four years, against the Oakland Raiders at the Rose Bowl in
Pasadena, California, on January 9, 1977. The Vikings, however, lost 32–14.[1]
- List of topics characterized as pseudoscience Criticism of pseudoscience, generally
by the scientific community or skeptical organizations, involves critiques of
the logical, methodological, or rhetorical bases of the topic in question.[1]
Though some of the listed topics continue to be investigated scientifically, others
were only subject to scientific research in the past, and today are considered
refuted but resurrected in a pseudoscientific fashion. Other ideas presented here
are entirely non-scientific, but have in one way or another infringed on scientific
domains or practices.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/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](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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': 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()
)
```
# Fine-Tuned BGE-Small Model for Q&A
This is a `BAAI/bge-small-en-v1.5` model that has been fine-tuned for a specific Question & Answering task using the `MultipleNegativesRankingLoss` in the `sentence-transformers` library.
It has been trained on a private dataset of 100,000+ question-answer pairs. Its primary purpose is to be the retriever model in a Retrieval-Augmented Generation (RAG) system. It excels at mapping questions to the passages that contain their answers.
## How to Use (Practical Inference Example)
The primary use case is to find the most relevant passage for a given query.
```python
from sentence_transformers import SentenceTransformer, util
# Load the fine-tuned model from the Hub
model_id = "srinivasanAI/bge-small-my-qna-model" # Replace with your model ID
model = SentenceTransformer(model_id)
# The BGE model requires a specific instruction for retrieval queries
instruction = "Represent this sentence for searching relevant passages: "
# 1. Define your query and your potential answers (passages)
query = instruction + "What is the powerhouse of the cell?"
passages = [
"Mitochondria are organelles that act like a digestive system and are often called the powerhouse of the cell.",
"The cell wall is a rigid layer that provides structural support to plant cells.",
"The sun is a star at the center of the Solar System."
]
# 2. Encode the single query and the list of passages separately
query_embedding = model.encode(query)
passage_embeddings = model.encode(passages)
# 3. Calculate the similarity between the single query and all passages
similarities = util.cos_sim(query_embedding, passage_embeddings)
# 4. Print the results
print(f"Query: {query.replace(instruction, '')}\\n")
for i, passage in enumerate(passages):
print(f"Similarity: {similarities[0][i]:.4f} | Passage: {passage}")
```
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### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 100,231 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: 18 tokens</li><li>mean: 19.69 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 139.68 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Represent this sentence for searching relevant passages: where did strangers prey at night take place</code> | <code>The Strangers: Prey at Night In a secluded trailer park in Salem, Arkansas, the three masked killers, The Walker family — Dollface, Pin Up Girl, and the Man in the Mask — arrive. Dollface kills a female occupant and then lies down in bed next to the woman's sleeping husband.</code> |
| <code>Represent this sentence for searching relevant passages: what is the average height of the highest peaks in the drakensberg mountain range</code> | <code>Drakensberg During the past 20 million years, further massive upliftment, especially in the East, has taken place in Southern Africa. As a result, most of the plateau lies above 1,000 m (3,300 ft) despite the extensive erosion. The plateau is tilted such that its highest point is in the east, and it slopes gently downwards towards the west and south. The elevation of the edge of the eastern escarpments is typically in excess of 2,000 m (6,600 ft). It reaches its highest point (over 3,000 m (9,800 ft)) where the escarpment forms part of the international border between Lesotho and the South African province of KwaZulu-Natal.[5][8]</code> |
| <code>Represent this sentence for searching relevant passages: name the two epics of india which are woven around with legends</code> | <code>Indian epic poetry Indian epic poetry is the epic poetry written in the Indian subcontinent, traditionally called Kavya (or Kāvya; Sanskrit: काव्य, IAST: kāvyá). The Ramayana and the Mahabharata, which were originally composed in Sanskrit and later translated into many other Indian languages, and The Five Great Epics of Tamil Literature and Sangam literature are some of the oldest surviving epic poems ever written.[1]</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
- `num_train_epochs`: 1
- `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`: 1
- `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}
- `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
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `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 |
|:------:|:----:|:-------------:|
| 0.1596 | 500 | 0.0556 |
| 0.3192 | 1000 | 0.0245 |
| 0.4788 | 1500 | 0.0236 |
| 0.6384 | 2000 | 0.0179 |
| 0.7980 | 2500 | 0.0202 |
| 0.9575 | 3000 | 0.0184 |
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.53.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.9.0
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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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