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language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:59315
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Johnny Depp plays policeman Ichabod Crane in which 1999 film?
sentences:
- 'Mythology in France epics and fairy tales as part of deeply embedded spiritual
allegories and mythological archetypes: Mythology in France The mythologies in
present-day France encompass the mythology of the Gauls, Franks, Normans, Bretons,
and other peoples living in France, those ancient stories about divine or heroic
beings that these particular cultures believed to be true and that often use supernatural
events or characters to explain the nature of the universe and humanity. French
mythology is listed for each culture. Bretons are a subset of the celtics that
adopted Christianity. Celtic cosmology predominates their mythology: Gauls were
another subset of Celtic people. Celtic'
- Johnny Depp in a snuff film in exchange for money for his family. Depp was a fan
and friend of writer Hunter S. Thompson, and played his alter ego Raoul Duke in
"Fear and Loathing in Las Vegas" (1998), Terry Gilliam's film adaptation of Thompson's
pseudobiographical novel of the same name. Depp's next venture with Burton was
the period film "Sleepy Hollow" (1999), in which he played Ichabod Crane opposite
Christina Ricci and Christopher Walken. For his performance, Depp took inspiration
from Angela Lansbury, Roddy McDowall and Basil Rathbone. He stated that he "always
thought of Ichabod as a very delicate, fragile
- Ichabod Crane Kinderhook town school district (Ichabod Crane Central School District)
is also named for the Irving character. It is claimed by many in Tarrytown that
Samuel Youngs is the original from whom Irving drew his character of Ichabod Crane".
Author Gary Denis asserts that while the character of Ichabod Crane is loosely
based on Kinderhook Schoolmaster, Jesse Merwin, it may possibly include elements
from Samuel Youngs' life. Irving's characters drive the story and are most memorable
because of his detail in describing each. He says of Ichabod Crane (the main character),
'He was tall, but exceedingly lank, with narrow shoulders, long
- source_sentence: What is the name of Liam Gallagher's new band, whose first album
is 'Different Gear, Still Speeding'?
sentences:
- Different Gear, Still Speeding Different Gear, Still Speeding Different Gear,
Still Speeding is the debut studio album by English rock band Beady Eye, released
on 28 February 2011. It debuted at number three in the UK Albums Chart selling
66,817 in the first week. As of August 2012, the album has sold 174,487 copies
in the UK. On "Different Gear, Still Speeding", all members contributed to the
instrumentation, much like the later albums of Oasis. Reviews of the album have
been generally mixed-to-favourable. According to review aggregator site Metacritic,
the album has an average score of 65%. Reviewing for "Rolling Stone", Stacey Anderson,
who
- Mickey Mouse universe was originally going to be named Mortimer. Lillian Disney,
Walt's wife, suggested the name Mickey instead. The first Mortimer was created
by Walt Disney and Floyd Gottfredson for the comics. He was Minnie Mouse's ranch-owning
cattleman uncle. He first appeared in the serial "Mickey Mouse in Death Valley"
(1930). After that, he appeared or was referenced in many other Mickey Mouse comic
strip adventures in the 1930s. He has occasionally appeared in more modern comics.
In the 1936 cartoon short "Mickey's Rival", the second Mortimer was introduced
as Mickey's competitor for Minnie's affections. In the comics, this Mortimer was
briefly
- Different Gear, Still Speeding gave the album 2.5 out of 5 stars, said "On Different
Gear, the band attempts stripped down, Stones-y rock but ends up with 'Be Here
Now'-style guitar bluster and Liam's blithely boilerplate lyrics". Drowned in
Sound awarded the album 4/10 saying that "By and large it radiates the stolid
competence of a band on auto-pilot, with a few flashes of likeable enthusiasm."
The "Independent on Sunday" gave it 2/5 stars. Simon Goddard, reviewing for "Q",
gave the album four-out-of-five stars and described it as "the strongest record
Liam's made" since "(What's the Story) Morning Glory?", while Garry Mulholland,
in his
- source_sentence: The bands R.E.M. and the B52s both come from which town in Georgia?
sentences:
- 'E and M signaling by the resistance of the wire, but will normally be less than
100m for adequate noise immunity. The group of E&M signaling includes the following
variations: E&M defines eight wires: "4-wire E&M" uses a 4-wire (2-pair) transmission
path for the voice signal. "2-wire E&M" uses a single pair for both transmit and
receive voice signal. This is much inferior to 4-wire E&M as the 2-wire interface
uses hybrid transformers which reduce signal quality and can introduce echo. The
mechanisms described so far only allow circuit seizure – on-hook and off-hook
– to be signaled. In order to allow dialing over'
- Culture of Georgia (U.S. state) a fertile field for alternative rock bands since
the late 1970s. Notable bands from Athens include R.E.M., The B-52's, Widespread
Panic, Drive-By Truckers, as well as bands from the Elephant 6 Recording Company
most notably Neutral Milk Hotel. Rhythm and Blues is another important musical
genre in Georgia. Ray Charles was one of popular music's most influential performers,
fusing R&B, jazz, and country into many popular songs. Augusta native James Brown
and Macon native Little Richard, two important figures in R&B history, started
performing in Georgia clubs on the chitlin' circuit, fused gospel with blues and
boogie-woogie to lay the
- 'I Just Shot John Lennon I Just Shot John Lennon "I Just Shot John Lennon" is
a song from The Cranberries'' album "To the Faithful Departed". It is a narrative
of the events of the night of December 8, 1980, the night that musician John Lennon
was murdered by Mark David Chapman in front of The Dakota in New York City. It
is one of many tributes to Lennon, and also one of many other songs to recall
the events of the night. After the narrative, there is commentary: "What a sad,
and sorry and sickening sight". The title of the song comes from the'
- source_sentence: Who wrote Three Men In A Boat?
sentences:
- Three Men in a Boat Three Men in a Boat Three Men in a Boat (To Say Nothing of
the Dog), published in 1889, is a humorous account by English writer Jerome K.
Jerome of a two-week boating holiday on the Thames from Kingston upon Thames to
Oxford and back to Kingston. The book was initially intended to be a serious travel
guide, with accounts of local history along the route, but the humorous elements
took over to the point where the serious and somewhat sentimental passages seem
a distraction to the comic novel. One of the most praised things about "Three
Men in a
- 'Darwin, Northern Territory line. Darwin lies in the Northern Territory, on the
Timor Sea. The city proper occupies a low bluff overlooking Darwin Harbour, flanked
by Frances Bay to the east and Cullen Bay to the west. The remainder of the city
is flat and low-lying, and coastal areas are home to recreational reserves, extensive
beaches, and excellent fishing. Darwin is closer to the capitals of five other
countries than to the capital of Australia: Darwin is away from Canberra. Dili
(East Timor) is , Port Moresby (Papua New Guinea) is , Jakarta (Indonesia) is
, Bandar Seri Begawan (Brunei) is , and'
- 'Three Men in a Boat 1891, "Three Women in One Boat: A River Sketch" by Constance
MacEwen was published. This book relates the journey of three young university
women who set out to emulate the river trip in "Three Men in a Boat" in an effort
to raise the spirits of one of them, who is about to be expelled from university.
To take the place of Montmorency, they bring a cat called Tintoretto. Three Men
in a Boat is referenced in the 1956 parody novel on mountaineering, "The Ascent
of Rum Doodle", where the head porter Bing is said to spend "much of his'
- source_sentence: In the RAF, what is the rank immediately above Squadron Leader?
sentences:
- Tybalt Tybalt Tybalt is the main antagonist in William Shakespeare's play "Romeo
and Juliet". He is the son of Lady Capulet's brother, Juliet's short-tempered
first cousin, and Romeo's rival. Tybalt shares the same name as the character
Tibert/Tybalt the "Prince of Cats" in "Reynard the Fox", a point of mockery in
the play. Mercutio repeatedly calls Tybalt "Prince of Cats" (perhaps referring
not only to Reynard but to the Italian word cazzo as well). Luigi da Porto adapted
the story as "Giulietta e Romeo" and included it in his "Historia novellamente
ritrovata di due Nobili Amanti" published in 1530. Da Porto
- Squadron leader RAF used major as the equivalent rank to squadron leader. Royal
Naval Air Service lieutenant-commanders and Royal Flying Corps majors on 31 March
1918 became RAF majors on 1 April 1918. On 31 August 1919, the RAF rank of major
was superseded by squadron leader which has remained in continuous usage ever
since. Promotion to squadron leader is strictly on merit, and requires the individual
to be appointed to a Career Commission, which will see them remain in the RAF
until retirement or voluntary resignation. Before the Second World War, a squadron
leader commanded a squadron of aircraft. Today, however,
- Squadron leader Squadron leader Squadron leader (Sqn Ldr in the RAF ; SQNLDR in
the RAAF and RNZAF; formerly sometimes S/L in all services) is a commissioned
rank in the Royal Air Force and the air forces of many countries which have historical
British influence. It is also sometimes used as the English translation of an
equivalent rank in countries which have a non-English air force-specific rank
structure. An air force squadron leader ranks above flight lieutenant and immediately
below wing commander and it is the most junior of the senior officer ranks. The
air force rank of squadron leader has a
datasets:
- sentence-transformers/trivia-qa-triplet
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: triplet
name: Triplet
dataset:
name: trivia qa eval
type: trivia_qa_eval
metrics:
- type: cosine_accuracy
value: 0.8339999914169312
name: Cosine Accuracy
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [trivia-qa-triplet](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) dataset. 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [trivia-qa-triplet](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'In the RAF, what is the rank immediately above Squadron Leader?',
'Squadron leader Squadron leader Squadron leader (Sqn Ldr in the RAF ; SQNLDR in the RAAF and RNZAF; formerly sometimes S/L in all services) is a commissioned rank in the Royal Air Force and the air forces of many countries which have historical British influence. It is also sometimes used as the English translation of an equivalent rank in countries which have a non-English air force-specific rank structure. An air force squadron leader ranks above flight lieutenant and immediately below wing commander and it is the most junior of the senior officer ranks. The air force rank of squadron leader has a',
'Squadron leader RAF used major as the equivalent rank to squadron leader. Royal Naval Air Service lieutenant-commanders and Royal Flying Corps majors on 31 March 1918 became RAF majors on 1 April 1918. On 31 August 1919, the RAF rank of major was superseded by squadron leader which has remained in continuous usage ever since. Promotion to squadron leader is strictly on merit, and requires the individual to be appointed to a Career Commission, which will see them remain in the RAF until retirement or voluntary resignation. Before the Second World War, a squadron leader commanded a squadron of aircraft. Today, however,',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5605, 0.4394],
# [0.5605, 1.0000, 0.5768],
# [0.4394, 0.5768, 1.0000]])
```
<!--
### 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: `trivia_qa_eval`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:----------|
| **cosine_accuracy** | **0.834** |
<!--
## 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
#### trivia-qa-triplet
* Dataset: [trivia-qa-triplet](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) at [bfe9460](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet/tree/bfe94607eb149a89fe8107e8c8d187977e587a7d)
* Size: 59,315 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 20.08 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 114 tokens</li><li>mean: 139.64 tokens</li><li>max: 226 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 138.81 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>"Sirius otherwise known as the ""Dog Star"" can be found in which constellation?"</code> | <code>Sirius in fiction below)—and even if such were eventually discovered, with an estimated age of 230 million years the system is too young to have fostered the development of life or a complex biosphere. Sirius AB is the alpha star of the constellation Canis Major (the great dog, sometimes styled as Orion's hunting dog), whence its cognomen "the dog star". The most commonly used proper name of this star comes through the Latin "Sirius", from the Greek Σείριος ("Seirios", "glowing" or "scorcher"). The ancient Greeks observed that the appearance of Sirius heralded the hot and dry "dog days" of summer, and feared that</code> | <code>Dog days "Seírios", "Scorcher"), Sothis (, "Sôthis", a transcription of Egyptian "Spdt"), and the Dog Star (, "Kúōn"). The last name reflects the way Sirius follows the constellation Orion into the night sky. Sirius is by far the brightest proper star in the night sky, which caused ancient astronomers to take note of it around the world. In Egypt, its return to the night sky became known as a precursor to the annual flooding of the Nile and was worshipped as the goddess Sopdet. In Greece, it became known as the precursor of the unpleasantly hot phase of the summer. Greek poets</code> |
| <code>Which Scottish town is the administrative centre of the Highland Region?</code> | <code>Highland Scottish All operations are now part of the Stagecoach Group. Highland Scottish Highland Scottish Omnibuses Ltd was formed as a bus operating subsidiary of the Scottish Transport Group in June 1985 from Highland Omnibuses Ltd, and operated until October 1995 when the company was split into two - Highland Bus & Coach and Highland Country Buses. The companies have since remerged and operate today as Highland Country Buses. From its head office in Seafield Road, Inverness, Highland Scottish operated over the massive geographical, but sparsely populated, area of the Highland region of north west Scotland. Highland Scottish was the largest operator</code> | <code>Highland Scottish in the centre of its 'Highland Country' logo. In January 1996 Highland Country Buses was bought by National Express for £1.8m. Highland Bus & Coach, being the smaller of the two operators, continued to operate with the image its predecessor adopted. The two companies continued to exist under separate ownership until August 1998 when Rapson's bought Highland Country Buses back from National Express for £4m - £2.2m more than Rapson's originally sold the company for. Highland Country Buses is now a wholly owned subsidiary of Rapson's Coaches, and covers the operating area that Highland Scottish had on privatisation. The company</code> |
| <code>Which brand of coffee is named after a hotel in Nashville, Tennessee?</code> | <code>History of Nashville, Tennessee proprietor thereof, had served a special blend of coffee at the hotel's restaurant, and after drinking a cup of this coffee, Roosevelt proclaimed it "good to the last drop!" Cheek subsequently sold the blend to General Foods and to this day, Maxwell House coffee is enjoyed by millions. In 1913 Nashville was the last of several major cities in Tennessee to adopt a commission form of government, with all members of a small commission elected at-large. Compared to single-member districts, this change resulted in further limiting the political power of any African Americans who were able to vote, as their</code> | <code>Renaissance Nashville Hotel Renaissance Nashville Hotel The Renaissance Nashville Hotel is a hotel in Nashville, Tennessee. The building is 385 feet high with 31 floors. The hotel is physically connected to the Nashville Convention Center and is its anchor hotel. The hotel contains 649 rooms, 24 suites, 25 meeting rooms with 31,000 sq ft of meeting space, and 2 concierge levels including a Starbucks coffee shop, 2 lounges, and a full service restaurant. One of the lounges is located in an enclosed bridge walkway, spanning above Commerce Street, which connects the hotel to a parking garage across the street. This walkway was severely</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",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### trivia-qa-triplet
* Dataset: [trivia-qa-triplet](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) at [bfe9460](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet/tree/bfe94607eb149a89fe8107e8c8d187977e587a7d)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 20.18 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 111 tokens</li><li>mean: 139.08 tokens</li><li>max: 225 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 138.94 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What African country is projected to pass the United States in population by the year 2055?</code> | <code>African immigration to the United States entering the United States. It is estimated that the current population of African immigrants to the United States is about 2.1 million. According to the Migration Policy Institute, as of 2009 two-thirds of the African immigrants were from either East or West Africa. Countries with the most immigrants to the U.S. are Nigeria, Egypt, Ethiopia, Ghana, South Africa, Somalia, Eritrea, and Kenya. Seventy five percent (75%) of the African immigrants to the U.S. come from 12 of the 55 countries, namely Nigeria, Egypt, Ghana, Ethiopia, South Africa, Kenya, Liberia, Somalia, Morocco, Cape Verde, Sierra Leone and Sudan (including what is</code> | <code>African immigration to the United States African countries due to many skilled hard-working Africans leaving Africa to seek their economic fortunes in the U.S. mainly and elsewhere. One major factor that contributes to migration from Africa to the United States is labor opportunities. It has been relatively easier for African immigrants for Africans with advanced education to leave and enter international labor markets. In addition, many Africans move to the United States for advanced training. For example, doctors from different African nations would move to the U.S. in order to gain more economic opportunities compared to their home country. However, as more Africans emigrate to the</code> |
| <code>Which is the largest species of the turtle family?</code> | <code>Leatherback sea turtle Leatherback sea turtle The leatherback sea turtle ("Dermochelys coriacea"), sometimes called the lute turtle or leathery turtle or simply the luth, is the largest of all living turtles and is the fourth-heaviest modern reptile behind three crocodilians. It is the only living species in the genus Dermochelys and family Dermochelyidae. It can easily be differentiated from other modern sea turtles by its lack of a bony shell, hence the name. Instead, its carapace is covered by skin and oily flesh. "Dermochelys" is the only extant genus of the family Dermochelyidae. "Dermochelys coriacea" is the only species in genus "Dermochelys". The</code> | <code>Okavango mud turtle Okavango mud turtle The Okavango mud turtle (Okavango terrapin) ("Pelusios bechuanicus") is a species of turtle in the family Pelomedusidae endemic to Africa. It is found in Angola, Botswana, the Democratic Republic of the Congo, Namibia (Caprivi), Zambia, and Zimbabwe. Found in central Africa, central Angola, northeastern Namibia, northern Botswana, Zimbabwe, and Zambia The Okavango mud turtle is largest species of the genus "Pelusios". The carapace is oval and elongated, with a pronounced dome, and is evenly rounded at the edges which allows the turtle to appear as a smooth rock. The carapace is very dark, often almost black, and</code> |
| <code>How many gallons of beer are in an English barrel?</code> | <code>Barrel gallon for liquids (the corn gallon of 268.8 cubic inches for solids). In Britain, the wine gallon was replaced by the imperial gallon. The tierce later became the petrol barrel. The tun was originally 256 gallons, which explains from where the quarter, 8 bushels or 64 (wine) gallons, comes. Although it is common to refer to draught beer containers of any size as barrels, in the UK this is strictly correct only if the container holds 36 imperial gallons. The terms "keg" and "cask" refer to containers of any size, the distinction being that kegs are used for beers intended</code> | <code>Beer Barrel Polecats Percy Pomeroy (Eddie Laughton), and work together to flee the prison. They are ultimately captured, and sent to solitary confinement. After nearly half a century later, the graying trio are finally released as senior citizens, in which Curly quips upon leaving "You know what I'm-a gonna do? I'm gonna get myself a tall, big, beautiful bottle of beer!" Moe and Larry become irate and throw Curly back into the jail, leaving him there. The title "Beer Barrel Polecats" is a pun of the song "Beer Barrel Polka". The idea of producing and selling their own beer during Prohibition was borrowed</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",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `warmup_steps`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: 0.1
- `warmup_steps`: 0.1
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `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_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `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`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | trivia_qa_eval_cosine_accuracy |
|:----------:|:--------:|:-------------:|:---------------:|:------------------------------:|
| 0.0011 | 1 | 0.7126 | - | - |
| 0.0539 | 50 | 0.7164 | - | - |
| 0.1079 | 100 | 0.7126 | - | - |
| 0.1618 | 150 | 0.6888 | - | - |
| 0.2157 | 200 | 0.6802 | - | - |
| 0.2697 | 250 | 0.6422 | - | - |
| 0.3236 | 300 | 0.6562 | - | - |
| 0.3776 | 350 | 0.6356 | - | - |
| 0.4315 | 400 | 0.6532 | - | - |
| 0.4854 | 450 | 0.6106 | - | - |
| 0.5394 | 500 | 0.6104 | 0.5472 | 0.7970 |
| 0.5933 | 550 | 0.6301 | - | - |
| 0.6472 | 600 | 0.6259 | - | - |
| 0.7012 | 650 | 0.5759 | - | - |
| 0.7551 | 700 | 0.6089 | - | - |
| 0.8091 | 750 | 0.5835 | - | - |
| 0.8630 | 800 | 0.5890 | - | - |
| 0.9169 | 850 | 0.5577 | - | - |
| 0.9709 | 900 | 0.5569 | - | - |
| 1.0248 | 950 | 0.5427 | - | - |
| 1.0787 | 1000 | 0.4698 | 0.5046 | 0.8190 |
| 1.1327 | 1050 | 0.4662 | - | - |
| 1.1866 | 1100 | 0.4634 | - | - |
| 1.2406 | 1150 | 0.4597 | - | - |
| 1.2945 | 1200 | 0.4585 | - | - |
| 1.3484 | 1250 | 0.5140 | - | - |
| 1.4024 | 1300 | 0.4542 | - | - |
| 1.4563 | 1350 | 0.4579 | - | - |
| 1.5102 | 1400 | 0.4910 | - | - |
| 1.5642 | 1450 | 0.5067 | - | - |
| 1.6181 | 1500 | 0.4800 | 0.4875 | 0.8300 |
| 1.6721 | 1550 | 0.4638 | - | - |
| 1.7260 | 1600 | 0.4760 | - | - |
| 1.7799 | 1650 | 0.4699 | - | - |
| 1.8339 | 1700 | 0.4912 | - | - |
| 1.8878 | 1750 | 0.4726 | - | - |
| 1.9417 | 1800 | 0.4764 | - | - |
| 1.9957 | 1850 | 0.4802 | - | - |
| 2.0496 | 1900 | 0.3941 | - | - |
| 2.1036 | 1950 | 0.3991 | - | - |
| **2.1575** | **2000** | **0.4114** | **0.4734** | **0.838** |
| 2.2114 | 2050 | 0.3981 | - | - |
| 2.2654 | 2100 | 0.4023 | - | - |
| 2.3193 | 2150 | 0.3932 | - | - |
| 2.3732 | 2200 | 0.3887 | - | - |
| 2.4272 | 2250 | 0.3894 | - | - |
| 2.4811 | 2300 | 0.3858 | - | - |
| 2.5351 | 2350 | 0.3907 | - | - |
| 2.5890 | 2400 | 0.3934 | - | - |
| 2.6429 | 2450 | 0.3871 | - | - |
| 2.6969 | 2500 | 0.3763 | 0.4681 | 0.8310 |
| 2.7508 | 2550 | 0.3997 | - | - |
| 2.8047 | 2600 | 0.3941 | - | - |
| 2.8587 | 2650 | 0.3884 | - | - |
| 2.9126 | 2700 | 0.3771 | - | - |
| 2.9666 | 2750 | 0.4168 | - | - |
| 3.0205 | 2800 | 0.3722 | - | - |
| 3.0744 | 2850 | 0.3565 | - | - |
| 3.1284 | 2900 | 0.3499 | - | - |
| 3.1823 | 2950 | 0.3428 | - | - |
| 3.2362 | 3000 | 0.3583 | 0.4669 | 0.8320 |
| 3.2902 | 3050 | 0.3444 | - | - |
| 3.3441 | 3100 | 0.3252 | - | - |
| 3.3981 | 3150 | 0.3563 | - | - |
| 3.4520 | 3200 | 0.3465 | - | - |
| 3.5059 | 3250 | 0.3328 | - | - |
| 3.5599 | 3300 | 0.3438 | - | - |
| 3.6138 | 3350 | 0.3330 | - | - |
| 3.6677 | 3400 | 0.3567 | - | - |
| 3.7217 | 3450 | 0.3462 | - | - |
| 3.7756 | 3500 | 0.3435 | 0.4639 | 0.8340 |
| 3.8296 | 3550 | 0.3532 | - | - |
| 3.8835 | 3600 | 0.3480 | - | - |
| 3.9374 | 3650 | 0.3361 | - | - |
| 3.9914 | 3700 | 0.3628 | - | - |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.2
- Transformers: 5.0.0
- PyTorch: 2.9.0+cu128
- Accelerate: 1.12.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2
## 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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