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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:44288
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: What past president of the Barcelona club committed suicide in
1930?
sentences:
- In 1978, Josep Lluís Núñez became the first elected president of FC Barcelona,
and, since then, the members of Barcelona have elected the club president. The
process of electing a president of FC Barcelona was closely tied to Spain's transition
to democracy in 1974 and the end of Franco's dictatorship. The new president's
main objective was to develop Barcelona into a world-class club by giving it stability
both on and off the pitch. His presidency was to last for 22 years, and it deeply
affected the image of Barcelona, as Núñez held to a strict policy regarding wages
and discipline, letting go of such players as Maradona, Romário and Ronaldo rather
than meeting their demands.
- On 14 June 1925, in a spontaneous reaction against Primo de Rivera's dictatorship,
the crowd in the stadium jeered the Royal March. As a reprisal, the ground was
closed for six months and Gamper was forced to relinquish the presidency of the
club. This coincided with the transition to professional football, and, in 1926,
the directors of Barcelona publicly claimed, for the first time, to operate a
professional football club. On 3 July 1927, the club held a second testimonial
match for Paulino Alcántara, against the Spanish national team. To kick off the
match, local journalist and pilot Josep Canudas dropped the ball onto the pitch
from his airplane. In 1928, victory in the Spanish Cup was celebrated with a poem
titled "Oda a Platko", which was written by a member of the Generation of '27,
Rafael Alberti, inspired by the heroic performance of the Barcelona goalkeeper,
Franz Platko. On 23 June 1929, Barcelona won the inaugural Spanish League. A year
after winning the championship, on 30 July 1930, Gamper committed suicide after
a period of depression brought on by personal and financial problems.
- In 2003 a congressional committee called the FBI's organized crime informant program
"one of the greatest failures in the history of federal law enforcement." The
FBI allowed four innocent men to be convicted of the March 1965 gangland murder
of Edward "Teddy" Deegan in order to protect Vincent Flemmi, an FBI informant.
Three of the men were sentenced to death (which was later reduced to life in prison),
and the fourth defendant was sentenced to life in prison. Two of the four men
died in prison after serving almost 30 years, and two others were released after
serving 32 and 36 years. In July 2007, U.S. District Judge Nancy Gertner in Boston
found the bureau helped convict the four men using false witness account by mobster
Joseph Barboza. The U.S. Government was ordered to pay $100 million in damages
to the four defendants.
- source_sentence: The MBTA is also known as the what?
sentences:
- Towards the end of the season, Randy Jackson, the last remaining of the original
judges, announced that he would no longer serve as a judge to pursue other business
ventures. Both judges Mariah Carey and Nicki Minaj also decided to leave after
one season to focus on their music careers.
- With nearly a third of Bostonians using public transit for their commute to work,
Boston has the fifth-highest rate of public transit usage in the country. Boston's
subway system, the Massachusetts Bay Transportation Authority (MBTA—known as the
"T") operates the oldest underground rapid transit system in the Americas, and
is the fourth-busiest rapid transit system in the country, with 65.5 miles (105
km) of track on four lines. The MBTA also operates busy bus and commuter rail
networks, and water shuttles.
- With nearly a third of Bostonians using public transit for their commute to work,
Boston has the fifth-highest rate of public transit usage in the country. Boston's
subway system, the Massachusetts Bay Transportation Authority (MBTA—known as the
"T") operates the oldest underground rapid transit system in the Americas, and
is the fourth-busiest rapid transit system in the country, with 65.5 miles (105
km) of track on four lines. The MBTA also operates busy bus and commuter rail
networks, and water shuttles.
- source_sentence: What must a police officer recite to a suspect upon arrest/
sentences:
- 'In the light of Mother F. A. Forbes research and reference to Pope Saint Gregory''s
writings, it would appear that Athanasius was constrained to be Bishop: She writes
that when the Patriarch Alexander was on his death-bed he called Athanasius, who
fled fearing he would be constrained to be made Bishop. "When the Bishops of the
Church assembled to elect their new Patriarch, the whole Catholic population surrounded
the church, holding up their hands to Heaven and crying; "Give us Athanasius!"
The Bishops had nothing better. Athanasius was thus elected, as Gregory tells
us..." (Pope Gregory I, would have full access to the Vatican Archives).'
- The law of criminal procedure in the United States consists of a massive overlay
of federal constitutional case law interwoven with the federal and state statutes
that actually provide the foundation for the creation and operation of law enforcement
agencies and prison systems as well as the proceedings in criminal trials. Due
to the perennial inability of legislatures in the U.S. to enact statutes that
would actually force law enforcement officers to respect the constitutional rights
of criminal suspects and convicts, the federal judiciary gradually developed the
exclusionary rule as a method to enforce such rights. In turn, the exclusionary
rule spawned a family of judge-made remedies for the abuse of law enforcement
powers, of which the most famous is the Miranda warning. The writ of habeas corpus
is often used by suspects and convicts to challenge their detention, while the
Civil Rights Act of 1871 and Bivens actions are used by suspects to recover tort
damages for police brutality.
- The law of criminal procedure in the United States consists of a massive overlay
of federal constitutional case law interwoven with the federal and state statutes
that actually provide the foundation for the creation and operation of law enforcement
agencies and prison systems as well as the proceedings in criminal trials. Due
to the perennial inability of legislatures in the U.S. to enact statutes that
would actually force law enforcement officers to respect the constitutional rights
of criminal suspects and convicts, the federal judiciary gradually developed the
exclusionary rule as a method to enforce such rights. In turn, the exclusionary
rule spawned a family of judge-made remedies for the abuse of law enforcement
powers, of which the most famous is the Miranda warning. The writ of habeas corpus
is often used by suspects and convicts to challenge their detention, while the
Civil Rights Act of 1871 and Bivens actions are used by suspects to recover tort
damages for police brutality.
- source_sentence: If the st belongs to one morpheme, then the stop is what?
sentences:
- They are unaspirated for almost all speakers when immediately following word-initial
s, as in spill, still, skill. After an s elsewhere in a word they are normally
unaspirated as well, except sometimes in compound words. When the consonants in
a cluster like st are analyzed as belonging to different morphemes (heteromorphemic)
the stop is aspirated, but when they are analyzed as belonging to one morpheme
the stop is unaspirated.[citation needed] For instance, distend has unaspirated
[t] since it is not analyzed as two morphemes, but distaste has an aspirated middle
[tʰ] because it is analyzed as dis- + taste and the word taste has an aspirated
initial t.
- 'The term "Enlightenment" emerged in English in the later part of the 19th century,
with particular reference to French philosophy, as the equivalent of the French
term ''Lumières'' (used first by Dubos in 1733 and already well established by
1751). From Immanuel Kant''s 1784 essay "Beantwortung der Frage: Was ist Aufklärung?"
("Answering the Question: What is Enlightenment?") the German term became ''Aufklärung''
(aufklären = to illuminate; sich aufklären = to clear up). However, scholars have
never agreed on a definition of the Enlightenment, or on its chronological or
geographical extent. Terms like "les Lumières" (French), "illuminismo" (Italian),
"ilustración" (Spanish) and "Aufklärung" (German) referred to partly overlapping
movements. Not until the late nineteenth century did English scholars agree they
were talking about "the Enlightenment."'
- They are unaspirated for almost all speakers when immediately following word-initial
s, as in spill, still, skill. After an s elsewhere in a word they are normally
unaspirated as well, except sometimes in compound words. When the consonants in
a cluster like st are analyzed as belonging to different morphemes (heteromorphemic)
the stop is aspirated, but when they are analyzed as belonging to one morpheme
the stop is unaspirated.[citation needed] For instance, distend has unaspirated
[t] since it is not analyzed as two morphemes, but distaste has an aspirated middle
[tʰ] because it is analyzed as dis- + taste and the word taste has an aspirated
initial t.
- source_sentence: What did the composition of the cardinals consist of?
sentences:
- The largest plaza in Valencia is the Plaza del Ayuntamiento; it is home to the
City Hall (Ayuntamiento) on its western side and the central post office (Edificio
de Correos) on its eastern side, a cinema that shows classic movies, and many
restaurants and bars. The plaza is triangular in shape, with a large cement lot
at the southern end, normally surrounded by flower vendors. It serves as ground
zero during the Les Falles when the fireworks of the Mascletà can be heard every
afternoon. There is a large fountain at the northern end.
- Pope Sixtus V limited the number of cardinals to 70, comprising six cardinal bishops,
50 cardinal priests, and 14 cardinal deacons. Starting in the pontificate of Pope
John XXIII, that limit has been exceeded. At the start of 1971, Pope Paul VI set
the number of cardinal electors at a maximum of 120, but set no limit on the number
of cardinals generally. He also established a maximum age of eighty years for
electors. His action deprived twenty-five living cardinals, including the three
living cardinals elevated by Pope Pius XI, of the right to participate in a conclave.[citation
needed] Popes can dispense from church laws and have sometimes brought the number
of cardinals under the age of 80 to more than 120. Pope Paul VI also increased
the number of cardinal bishops by giving that rank to patriarchs of the Eastern
Catholic Churches.
- Pope Sixtus V limited the number of cardinals to 70, comprising six cardinal bishops,
50 cardinal priests, and 14 cardinal deacons. Starting in the pontificate of Pope
John XXIII, that limit has been exceeded. At the start of 1971, Pope Paul VI set
the number of cardinal electors at a maximum of 120, but set no limit on the number
of cardinals generally. He also established a maximum age of eighty years for
electors. His action deprived twenty-five living cardinals, including the three
living cardinals elevated by Pope Pius XI, of the right to participate in a conclave.[citation
needed] Popes can dispense from church laws and have sometimes brought the number
of cardinals under the age of 80 to more than 120. Pope Paul VI also increased
the number of cardinal bishops by giving that rank to patriarchs of the Eastern
Catholic Churches.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: triplet
name: Triplet
dataset:
name: gooqa dev
type: gooqa-dev
metrics:
- type: cosine_accuracy
value: 0.4065999984741211
name: Cosine Accuracy
---
# 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("ayushexel/emb-bge-base-en-v1.5-squad-8-epochs")
# Run inference
sentences = [
'What did the composition of the cardinals consist of?',
'Pope Sixtus V limited the number of cardinals to 70, comprising six cardinal bishops, 50 cardinal priests, and 14 cardinal deacons. Starting in the pontificate of Pope John XXIII, that limit has been exceeded. At the start of 1971, Pope Paul VI set the number of cardinal electors at a maximum of 120, but set no limit on the number of cardinals generally. He also established a maximum age of eighty years for electors. His action deprived twenty-five living cardinals, including the three living cardinals elevated by Pope Pius XI, of the right to participate in a conclave.[citation needed] Popes can dispense from church laws and have sometimes brought the number of cardinals under the age of 80 to more than 120. Pope Paul VI also increased the number of cardinal bishops by giving that rank to patriarchs of the Eastern Catholic Churches.',
'Pope Sixtus V limited the number of cardinals to 70, comprising six cardinal bishops, 50 cardinal priests, and 14 cardinal deacons. Starting in the pontificate of Pope John XXIII, that limit has been exceeded. At the start of 1971, Pope Paul VI set the number of cardinal electors at a maximum of 120, but set no limit on the number of cardinals generally. He also established a maximum age of eighty years for electors. His action deprived twenty-five living cardinals, including the three living cardinals elevated by Pope Pius XI, of the right to participate in a conclave.[citation needed] Popes can dispense from church laws and have sometimes brought the number of cardinals under the age of 80 to more than 120. Pope Paul VI also increased the number of cardinal bishops by giving that rank to patriarchs of the Eastern Catholic Churches.',
]
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `gooqa-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.4066** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 44,288 training samples
* Columns: <code>question</code>, <code>context</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | question | context | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.58 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 150.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 153.22 tokens</li><li>max: 481 tokens</li></ul> |
* Samples:
| question | context | negative |
|:-----------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How many judges were originally planned for American Idol?</code> | <code>The show had originally planned on having four judges following the Pop Idol format; however, only three judges had been found by the time of the audition round in the first season, namely Randy Jackson, Paula Abdul and Simon Cowell. A fourth judge, radio DJ Stryker, was originally chosen but he dropped out citing "image concerns". In the second season, New York radio personality Angie Martinez had been hired as a fourth judge but withdrew only after a few days of auditions due to not being comfortable with giving out criticism. The show decided to continue with the three judges format until season eight. All three original judges stayed on the judging panel for eight seasons.</code> | <code>On February 14, 2009, The Walt Disney Company debuted "The American Idol Experience" at its Disney's Hollywood Studios theme park at the Walt Disney World Resort in Florida. In this live production, co-produced by 19 Entertainment, park guests chose from a list of songs and auditioned privately for Disney cast members. Those selected then performed on a stage in a 1000-seat theater replicating the Idol set. Three judges, whose mannerisms and style mimicked those of the real Idol judges, critiqued the performances. Audience members then voted for their favorite performer. There were several preliminary-round shows during the day that culminated in a "finals" show in the evening where one of the winners of the previous rounds that day was selected as the overall winner. The winner of the finals show received a "Dream Ticket" that granted them front-of-the-line privileges at any future American Idol audition. The attraction closed on August 30, 2014.</code> |
| <code>What genre of music did season ten American Idol contestant Lauren Alaina sing?</code> | <code>The two finalists in 2011 were Lauren Alaina and Scotty McCreery, both teenage country singers. McCreery won the competition on May 25, being the youngest male winner and the fourth male in a row to win American Idol. McCreery released his first single, "I Love You This Big", as his coronation song, and Alaina released "Like My Mother Does". McCreery's debut album, Clear as Day, became the first debut album by an Idol winner to reach No. 1 on the US Billboard 200 since Ruben Studdard's Soulful in 2003, and he became the youngest male artist to reach No. 1 on the Billboard 200.</code> | <code>The impact of American Idol is also strongly felt in musical theatre, where many of Idol alumni have forged successful careers. The striking effect of former American Idol contestants on Broadway has been noted and commented on. The casting of a popular Idol contestant can lead to significantly increased ticket sales. Other alumni have gone on to work in television and films, the most notable being Jennifer Hudson who, on the recommendation of the Idol vocal coach Debra Byrd, won a role in Dreamgirls and subsequently received an Academy Award for her performance.</code> |
| <code>What was responsible for creating thousands of scientific, technological, and knowledge-based businesses?</code> | <code>With the emergence and growth of several science parks throughout the world that helped create many thousands of scientific, technological and knowledge-based businesses, Portugal started to develop several science parks across the country. These include the Taguspark (in Oeiras), the Coimbra iParque (in Coimbra), the biocant (in Cantanhede), the Madeira Tecnopolo (in Funchal), Sines Tecnopolo (in Sines), Tecmaia (in Maia) and Parkurbis (in Covilhã). Companies locate in the Portuguese science parks to take advantage of a variety of services ranging from financial and legal advice through to marketing and technological support.</code> | <code>Certain technological inventions of the period – whether of Arab or Chinese origin, or unique European innovations – were to have great influence on political and social developments, in particular gunpowder, the printing press and the compass. The introduction of gunpowder to the field of battle affected not only military organisation, but helped advance the nation state. Gutenberg's movable type printing press made possible not only the Reformation, but also a dissemination of knowledge that would lead to a gradually more egalitarian society. The compass, along with other innovations such as the cross-staff, the mariner's astrolabe, and advances in shipbuilding, enabled the navigation of the World Oceans, and the early phases of colonialism. Other inventions had a greater impact on everyday life, such as eyeglasses and the weight-driven clock.</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"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 5,000 evaluation samples
* Columns: <code>question</code>, <code>context</code>, and <code>negative_1</code>
* Approximate statistics based on the first 1000 samples:
| | question | context | negative_1 |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.64 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 153.83 tokens</li><li>max: 510 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 152.96 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| question | context | negative_1 |
|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What was the name of the first sulfonamine antibiotic?</code> | <code>Ehrlich’s approach of systematically varying the chemical structure of synthetic compounds and measuring the effects of these changes on biological activity was pursued broadly by industrial scientists, including Bayer scientists Josef Klarer, Fritz Mietzsch, and Gerhard Domagk. This work, also based in the testing of compounds available from the German dye industry, led to the development of Prontosil, the first representative of the sulfonamide class of antibiotics. Compared to arsphenamine, the sulfonamides had a broader spectrum of activity and were far less toxic, rendering them useful for infections caused by pathogens such as streptococci. In 1939, Domagk received the Nobel Prize in Medicine for this discovery. Nonetheless, the dramatic decrease in deaths from infectious diseases that occurred prior to World War II was primarily the result of improved public health measures such as clean water and less crowded housing, and the impact of anti-infective drugs and vaccines was sign...</code> | <code>The first sulfonamide and first commercially available antibacterial, Prontosil, was developed by a research team led by Gerhard Domagk in 1932 at the Bayer Laboratories of the IG Farben conglomerate in Germany. Domagk received the 1939 Nobel Prize for Medicine for his efforts. Prontosil had a relatively broad effect against Gram-positive cocci, but not against enterobacteria. Research was stimulated apace by its success. The discovery and development of this sulfonamide drug opened the era of antibacterials.</code> |
| <code>Who disregarded warnings about dams in the area?</code> | <code>An article in Science suggested that the construction and filling of the Zipingpu Dam may have triggered the earthquake. The chief engineer of the Sichuan Geology and Mineral Bureau said that the sudden shift of a huge quantity of water into the region could have relaxed the tension between the two sides of the fault, allowing them to move apart, and could have increased the direct pressure on it, causing a violent rupture. The effect was "25 times more" than a year's worth of natural stress from tectonic movement. The government had disregarded warnings about so many large-scale dam projects in a seismically active area. Researchers have been denied access to seismological and geological data to examine the cause of the quake further.</code> | <code>An article in Science suggested that the construction and filling of the Zipingpu Dam may have triggered the earthquake. The chief engineer of the Sichuan Geology and Mineral Bureau said that the sudden shift of a huge quantity of water into the region could have relaxed the tension between the two sides of the fault, allowing them to move apart, and could have increased the direct pressure on it, causing a violent rupture. The effect was "25 times more" than a year's worth of natural stress from tectonic movement. The government had disregarded warnings about so many large-scale dam projects in a seismically active area. Researchers have been denied access to seismological and geological data to examine the cause of the quake further.</code> |
| <code>What annual ceremony do Freemasons have?</code> | <code>The bulk of Masonic ritual consists of degree ceremonies. Candidates for Freemasonry are progressively initiated into Freemasonry, first in the degree of Entered Apprentice. Some time later, in a separate ceremony, they will be passed to the degree of Fellowcraft, and finally they will be raised to the degree of Master Mason. In all of these ceremonies, the candidate is entrusted with passwords, signs and grips peculiar to his new rank. Another ceremony is the annual installation of the Master and officers of the Lodge. In some jurisdictions Installed Master is valued as a separate rank, with its own secrets to distinguish its members. In other jurisdictions, the grade is not recognised, and no inner ceremony conveys new secrets during the installation of a new Master of the Lodge.</code> | <code>Freemasonry consists of fraternal organisations that trace their origins to the local fraternities of stonemasons, which from the end of the fourteenth century regulated the qualifications of stonemasons and their interaction with authorities and clients. The degrees of freemasonry retain the three grades of medieval craft guilds, those of Apprentice, Journeyman or fellow (now called Fellowcraft), and Master Mason. These are the degrees offered by Craft (or Blue Lodge) Freemasonry. Members of these organisations are known as Freemasons or Masons. There are additional degrees, which vary with locality and jurisdiction, and are usually administered by different bodies than the craft degrees.</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
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 8
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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`: 1.0
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | gooqa-dev_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:-------------------------:|
| -1 | -1 | - | - | 0.3564 |
| 0.2890 | 100 | 0.7631 | 0.8236 | 0.3766 |
| 0.5780 | 200 | 0.4816 | 0.7701 | 0.3962 |
| 0.8671 | 300 | 0.4197 | 0.7316 | 0.4012 |
| 1.1561 | 400 | 0.3274 | 0.7281 | 0.4104 |
| 1.4451 | 500 | 0.2834 | 0.7302 | 0.4078 |
| 1.7341 | 600 | 0.2677 | 0.7327 | 0.4036 |
| 2.0231 | 700 | 0.2654 | 0.7161 | 0.4122 |
| 2.3121 | 800 | 0.1517 | 0.7344 | 0.4094 |
| 2.6012 | 900 | 0.1558 | 0.7256 | 0.4174 |
| 2.8902 | 1000 | 0.1604 | 0.7256 | 0.4110 |
| 3.1792 | 1100 | 0.1214 | 0.7413 | 0.4110 |
| 3.4682 | 1200 | 0.1024 | 0.7434 | 0.4124 |
| 3.7572 | 1300 | 0.1064 | 0.7384 | 0.4126 |
| 4.0462 | 1400 | 0.1024 | 0.7465 | 0.4114 |
| 4.3353 | 1500 | 0.0742 | 0.7551 | 0.4180 |
| 4.6243 | 1600 | 0.0756 | 0.7664 | 0.4128 |
| 4.9133 | 1700 | 0.0761 | 0.7566 | 0.4136 |
| 5.2023 | 1800 | 0.0645 | 0.7629 | 0.4126 |
| 5.4913 | 1900 | 0.0589 | 0.7709 | 0.4160 |
| 5.7803 | 2000 | 0.061 | 0.7709 | 0.4122 |
| 6.0694 | 2100 | 0.0575 | 0.7735 | 0.4116 |
| 6.3584 | 2200 | 0.0484 | 0.7798 | 0.4134 |
| 6.6474 | 2300 | 0.0503 | 0.7820 | 0.4098 |
| 6.9364 | 2400 | 0.0505 | 0.7778 | 0.4086 |
| 7.2254 | 2500 | 0.0449 | 0.7826 | 0.4100 |
| 7.5145 | 2600 | 0.0449 | 0.7838 | 0.4082 |
| 7.8035 | 2700 | 0.0442 | 0.7864 | 0.4070 |
| -1 | -1 | - | - | 0.4066 |
### Framework Versions
- Python: 3.11.0
- Sentence Transformers: 4.0.1
- 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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