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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6433
- loss:Infonce
base_model: microsoft/mpnet-base
widget:
- source_sentence: What surprised the author about the appearance of sloths when looking
for animals to draw for the letter S
sentences:
- "Third National Bank may refer to:\n\nin the United States\n(by state)\nThird\
\ National Bank (Atlanta, Georgia), now The Metropolitan (Atlanta condominium\
\ building)\n Third National Bank (Glasgow, Kentucky), listed on the NRHP in Kentucky\n\
\ Third National Bank (Ohio), a predecessor of Fifth Third Bank\n Third National\
\ Bank (Syracuse, New York), listed on the NRHP in New York\n Third National Bank\
\ (Sandusky, Ohio), listed on the NRHP in Ohio\n Third National Bank in Nashville,\
\ now incorporated within SunTrust Bank"
- 'S is for Sloth
I never really paid much attention to sloths until I began searching for animals
to draw for the letter S. Looking at some photos, I was shocked to see just how
much they look like Muppets in real life! They''re hilarious!'
- 'History
The Annual Review of Animal Biosciences was first published in 2013, with Harris
A. Lewin and R. Michael Roberts as the founding co-editors. Though it was initially
published in print, as of 2021 it is only published electronically. Scope and
indexing
The Annual Review of Animal Biosciences defines its scope as covering significant
developments relevant to biotechnology, genomics, genetics, veterinary medicine,
animal breeding, and conservation biology. The intended audience for the journal
is scientists and veterinarians involved with wild and domestic animals. It is
abstracted and indexed in Scopus, Science Citation Index Expanded, MEDLINE, and
Embase, among others.'
- source_sentence: What distinguished the roles of different prisoners, such as the
functionaries and the Sonderkommando, in the Auschwitz camps
sentences:
- "Michael Matthews is a South African writer, producer and director. In 2017, he\
\ directed Five Fingers for Marseilles, a film that won best film category at\
\ 14th Africa Movie Academy Awards. Early life\nMatthews was born in Durban. He\
\ studied filmmaking at the CityVarsity Cape Town campus. Career \nMatthews is\
\ the director of Five Fingers for Marseilles, a film dubbed as South African\
\ first western film."
- Warsame and Ahmed M. Hassan, who was elected to the Clarkston, Georgia City Council
on the same day, are the first Somali Americans to be elected to municipal offices
in the United States and were the highest elected Somali Americans in the country
at the time. Warsame's election set civic precedence in the Somali American community
of Minneapolis, in which his campaign energized and mobilized this sub-community's
powerful voting bloc.
- 'Designated as Aussenlager (external camp), Nebenlager (extension camp), Arbeitslager
(labor camp), or Aussenkommando (external work detail), camps were built at Blechhammer,
Jawiszowice, Jaworzno, Lagisze, Mysłowice, Trzebinia, and as far afield as the
Protectorate of Bohemia and Moravia in Czechoslovakia. Industries with satellite
camps included coal mines, foundries and other metal works, and chemical plants.
Prisoners were also made to work in forestry and farming. For example, Wirtschaftshof
Budy, in the Polish village of Budy near Brzeszcze, was a farming subcamp where
prisoners worked 12-hour days in the fields, tending animals, and making compost
by mixing human ashes from the crematoria with sod and manure. Incidents of sabotage
to decrease production took place in several subcamps, including Charlottengrube,
Gleiwitz II, and Rajsko. Living conditions in some of the camps were so poor that
they were regarded as punishment subcamps. Life in the camps
SS garrison
Rudolf Höss, born in Baden-Baden in 1900, was named the first commandant of Auschwitz
when Heinrich Himmler ordered on 27 April 1940 that the camp be established. Living
with his wife and children in a two-story stucco house near the commandant''s
and administration building, he served as commandant until 11 November 1943, with
Josef Kramer as his deputy. Succeeded as commandant by Arthur Liebehenschel, Höss
joined the SS Business and Administration Head Office in Oranienburg as director
of Amt DI, a post that made him deputy of the camps inspectorate. Richard Baer
became commandant of Auschwitz I on 11 May 1944 and Fritz Hartjenstein of Auschwitz
II from 22 November 1943, followed by Josef Kramer from 15 May 1944 until the
camp''s liquidation in January 1945. Heinrich Schwarz was commandant of Auschwitz
III from the point at which it became an autonomous camp in November 1943 until
its liquidation. Höss returned to Auschwitz between 8 May and 29 July 1944 as
the local SS garrison commander (Standortältester) to oversee the arrival of Hungary''s
Jews, which made him the superior officer of all the commandants of the Auschwitz
camps. According to Aleksander Lasik, about 6,335 people (6,161 of them men) worked
for the SS at Auschwitz over the course of the camp''s existence; 4.2 percent
were officers, 26.1 percent non-commissioned officers, and 69.7 percent rank and
file. In March 1941, there were 700 SS guards; in June 1942, 2,000; and in August
1944, 3,342. At its peak in January 1945, 4,480 SS men and 71 SS women worked
in Auschwitz; the higher number is probably attributable to the logistics of evacuating
the camp. Female guards were known as SS supervisors (SS-Aufseherinnen). Most
of the staff were from Germany or Austria, but as the war progressed, increasing
numbers of Volksdeutsche from other countries, including Czechoslovakia, Poland,
Yugoslavia, and the Baltic states, joined the SS at Auschwitz. Not all were ethnically
German. Guards were also recruited from Hungary, Romania, and Slovakia. Camp guards,
around three quarters of the SS personnel, were members of the SS-Totenkopfverbände
(death''s head units). Other SS staff worked in the medical or political departments,
or in the economic administration, which was responsible for clothing and other
supplies, including the property of dead prisoners. The SS viewed Auschwitz as
a comfortable posting; being there meant they had avoided the front and had access
to the victims'' property. Functionaries and Sonderkommando
Certain prisoners, at first non-Jewish Germans but later Jews and non-Jewish Poles,
were assigned positions of authority as Funktionshäftlinge (functionaries), which
gave them access to better housing and food. The Lagerprominenz (camp elite) included
Blockschreiber (barracks clerk), Kapo (overseer), Stubendienst (barracks orderly),
and Kommandierte (trusties). Wielding tremendous power over other prisoners, the
functionaries developed a reputation as sadists. Very few were prosecuted after
the war, because of the difficulty of determining which atrocities had been performed
by order of the SS. Although the SS oversaw the murders at each gas chamber, the
forced labor portion of the work was done by prisoners known from 1942 as the
Sonderkommando (special squad). These were mostly Jews but they included groups
such as Soviet POWs. In 1940–1941 when there was one gas chamber, there were 20
such prisoners, in late 1943 there were 400, and by 1944 during the Holocaust
in Hungary the number had risen to 874. The Sonderkommando removed goods and corpses
from the incoming trains, guided victims to the dressing rooms and gas chambers,
removed their bodies afterwards, and took their jewelry, hair, dental work, and
any precious metals from their teeth, all of which was sent to Germany. Once the
bodies were stripped of anything valuable, the Sonderkommando burned them in the
crematoria. Because they were witnesses to the mass murder, the Sonderkommando
lived separately from the other prisoners, although this rule was not applied
to the non-Jews among them. Their quality of life was further improved by their
access to the property of new arrivals, which they traded within the camp, including
with the SS. Nevertheless, their life expectancy was short; they were regularly
murdered and replaced. About 100 survived to the camp''s liquidation. They were
forced on a death march and by train to the camp at Mauthausen, where three days
later they were asked to step forward during roll call. No one did, and because
the SS did not have their records, several of them survived. Tattoos and triangles
Uniquely at Auschwitz, prisoners were tattooed with a serial number, on their
left breast for Soviet prisoners of war and on the left arm for civilians. Categories
of prisoner were distinguishable by triangular pieces of cloth (German: Winkel)
sewn onto on their jackets below their prisoner number. Political prisoners (Schutzhäftlinge
or Sch), mostly Poles, had a red triangle, while criminals (Berufsverbrecher or
BV) were mostly German and wore green. Asocial prisoners (Asoziale or Aso), which
included vagrants, prostitutes and the Roma, wore black. Purple was for Jehovah''s
Witnesses (Internationale Bibelforscher-Vereinigung or IBV)''s and pink for gay
men, who were mostly German. An estimated 5,000–15,000 gay men prosecuted under
German Penal Code Section 175 (proscribing sexual acts between men) were detained
in concentration camps, of whom an unknown number were sent to Auschwitz. Jews
wore a yellow badge, the shape of the Star of David, overlaid by a second triangle
if they also belonged to a second category. The nationality of the inmate was
indicated by a letter stitched onto the cloth. A racial hierarchy existed, with
German prisoners at the top. Next were non-Jewish prisoners from other countries.
Jewish prisoners were at the bottom. Transports
Deportees were brought to Auschwitz crammed in wretched conditions into goods
or cattle wagons, arriving near a railway station or at one of several dedicated
trackside ramps, including one next to Auschwitz I. The Altejudenrampe (old Jewish
ramp), part of the Oświęcim freight railway station, was used from 1942 to 1944
for Jewish transports. Located between Auschwitz I and Auschwitz II, arriving
at this ramp meant a 2.5 km journey to Auschwitz II and the gas chambers. Most
deportees were forced to walk, accompanied by SS men and a car with a Red Cross
symbol that carried the Zyklon B, as well as an SS doctor in case officers were
poisoned by mistake. Inmates arriving at night, or who were too weak to walk,
were taken by truck. Work on a new railway line and ramp (right) between sectors
BI and BII in Auschwitz II, was completed in May 1944 for the arrival of Hungarian
Jews between May and early July 1944. The rails led directly to the area around
the gas chambers. Life for the inmates
The day began at 4:30 am for the men (an hour later in winter), and earlier for
the women, when the block supervis'
- source_sentence: Do restaurants like Chick-fil-A have signs indicating restrictions
against LGBTQ+ individuals
sentences:
- 'Restaurants have signs like "no smoking," "no guns," "no shoes, no service,"
but never have I seen a restaurant, especially Chick-fil-A, say "no gays or lesbians."
Get on the ball
In the newspaper this past week, "St. Charles seeks input on mall," how many more
studies are they going to do'
- His excavations lead to him being convinced that this site was more than likely
a pre-ceramic age and decided to discover it further. Later Voorhies worked to
understand and evaluate the Chantuto sites and the people who inhabited this area.
- Gross domestic product (GDP) is the market value of all final goods and services
from a nation in a given year.
- source_sentence: How can Vaseline be used to help with chapped lips
sentences:
- '4. Soothe Chapped Lips
So, you probably already knew Vaseline made a great lip balm, but it can also
be used as a base in many lip scrubs, which will really come in handy during the
winter months. 5.'
- Retrieved March 30, 2005 from . Healthcare Information and Management Systems
Society (HIMSS) (2005, February).
- The Government of Zimbabwe strongly believes in the independence of the judiciary
and respects the principles of the separation of powers as set out in the Constitution
of Zimbabwe. The Government of Zimbabwe, therefore, recognises the importance
of the judiciary as a dependable interpreter of the law where various opinions
may arise.
- source_sentence: What challenges do university researchers face when trying to turn
their discoveries into commercial products
sentences:
- 'A major shakeup has taken place at the top of the Boston Celtics. Danny Ainge
has stepped down as president of basketball operations, and head coach Brad Stevens
has stepped into the role. Stevens will now lead the search for a new coach. The
team made the announcement early Wednesday, one day after the Celtics were eliminated
by the Brooklyn Nets in the first round of the Eastern Conference playoffs. “Helping
guide this organization has been the thrill of a lifetime, and having worked side-by-side
with him since he’s been here, I know we couldn’t be in better hands than with
Brad guiding the team going forward,” Ainge said in a statement. “I’m grateful
to ownership, all of my Celtics colleagues, and the best fans in basketball for
being part of the journey.”
Ainge, 62, is a franchise legend.'
- 'Alfred William Lawson (March 24, 1869 – November 29, 1954) was an English born
professional baseball player, aviator and utopian philosopher. He was a baseball
player, manager, and league promoter from 1887 through 1916 and went on to play
a pioneering role in the U.S. aircraft industry. He published two early aviation
trade journals. He is frequently cited as the inventor of the airliner and was
awarded several of the first air mail contracts, which he ultimately could not
fulfill. He founded the Lawson Aircraft Company in Green Bay, Wisconsin, to build
military training aircraft and later the Lawson Airplane Company in South Milwaukee,
Wisconsin, to build airliners. The crash of his ambitious Lawson L-4 "Midnight
Liner" during its trial flight takeoff on May 8, 1921, ended his best chance
for commercial aviation success. In 1904, he wrote a utopian novel, Born Again,
in which he developed the philosophy which later became Lawsonomy. Baseball career
(1888–1907)
He made one start for the Boston Beaneaters and two for the Pittsburgh Alleghenys
during the 1890 season. His minor league playing career lasted through 1895. He
later managed in the minors from 1905 to 1907. Union Professional League
In 1908, he started a new professional baseball league known as the Union Professional
League. The league took the field in April but folded one month later owing to
financial difficulties. Aviation career (1908–1928)
An early advocate or rather evangelist of aviation, in October 1908 Lawson started
the magazine Fly to stimulate public interest and educate readers in the fundamentals
of the new science of aviation. It sold for 10 cents a copy from newsstands across
the country. In 1910, moving to New York City, he renamed the magazine Aircraft
and published it until 1914. The magazine chronicled the technical developments
of the early aviation pioneers. Lawson was the first advocate for commercial air
travel, coining the term "airline." He also advocated for a strong American flying
force, lobbying Congress in 1913 to expand its appropriations for Army aircraft.
In early 1913, he learned to fly the Sloan-Deperdussin and the Moisant-Bleriot
monoplanes, becoming an accomplished pilot. Later that year he bought a Thomas
flying boat and became the first air commuter regularly flying from his country
house in Seidler''s Beach, New Jersey, to the foot of 75th Street in New York
City (about 35 miles). In 1917, utilizing the knowledge gained from ten years
of advocating aviation, he built his first airplane, the Lawson Military Tractor
1 (MT-1) trainer, and founded the Lawson Aircraft Corporation. The company''s
plant was sited at Green Bay, Wisconsin. There he secured a contract and built
the Lawson MT-2. He also designed the steel fuselage Lawson Armored Battler, which
never got beyond the drafting board, given doubts within the Army aviation community
and the signing of the armistice. After the war, in 1919 Lawson started a project
to build America''s first airline. He secured financial backing, and in five months
he had built and demonstrated in flight his biplane airliner, the 18-passenger
Lawson L-2. He demonstrated its capabilities in a 2000-mile multi-city tour from
Milwaukee to Chicago-Toledo-Cleveland-Buffalo-Syracuse-New york City-Washington,
D.C.-Collinsville-Dayton-Chicago and back to Milwaukee, creating a buzz of positive
press. The publicity allowed him to secure an additional $1 million to build the
26-passenger Midnight Liner. The aircraft crashed on takeoff on its maiden flight.
In late 1920, he secured government contracts for three airmail routes and to
deliver ten war planes, but owing to the fall 1920 recession, he could not secure
the necessary $100,000 in cash reserves called for in the contracts and had to
decline them.'
- 'Universities are vital to the process of innovation and advancement: they educate
students who bring new ways of thinking to old problems, and they make new discoveries
that no one else would make because no one else has the opportunity to delve so
deeply. In creating this type of refuge, we also create a comfort zone. Because
governmental support for science and technology is designed to support long-term,
high-risk work regardless of immediate return, ROI is not a factor in getting
government funding. University researchers become successful at pitching research
ideas without serious reference to commercial outcome. Peer review – which is
critical for the success of science – further reinforces this tendency. University
researchers are rewarded for thinking in this very specific way, and this creates
the comfort zone. As it dawns on a researcher that they may need to work with
a company or an entrepreneur to see their discoveries become products or services
that can benefit society, they may find themselves a victim of their own past
success. Many researchers reflexively approach companies as if they are yet another
type of funding agency, but since companies are not in the grant-making business,
a partnership fails to materialize. This basic failure to communicate means valuable
commercial opportunities are often not recognized, or when they are, the resulting
partnership does not go well.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# MPNet base trained on AllNLI triplets
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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("Jrinky/mpnet-base-all-nli-triplet")
# Run inference
sentences = [
'What challenges do university researchers face when trying to turn their discoveries into commercial products',
'Universities are vital to the process of innovation and advancement: they educate students who bring new ways of thinking to old problems, and they make new discoveries that no one else would make because no one else has the opportunity to delve so deeply. In creating this type of refuge, we also create a comfort zone. Because governmental support for science and technology is designed to support long-term, high-risk work regardless of immediate return, ROI is not a factor in getting government funding. University researchers become successful at pitching research ideas without serious reference to commercial outcome. Peer review – which is critical for the success of science – further reinforces this tendency. University researchers are rewarded for thinking in this very specific way, and this creates the comfort zone. As it dawns on a researcher that they may need to work with a company or an entrepreneur to see their discoveries become products or services that can benefit society, they may find themselves a victim of their own past success. Many researchers reflexively approach companies as if they are yet another type of funding agency, but since companies are not in the grant-making business, a partnership fails to materialize. This basic failure to communicate means valuable commercial opportunities are often not recognized, or when they are, the resulting partnership does not go well.',
'A major shakeup has taken place at the top of the Boston Celtics. Danny Ainge has stepped down as president of basketball operations, and head coach Brad Stevens has stepped into the role. Stevens will now lead the search for a new coach. The team made the announcement early Wednesday, one day after the Celtics were eliminated by the Brooklyn Nets in the first round of the Eastern Conference playoffs. “Helping guide this organization has been the thrill of a lifetime, and having worked side-by-side with him since he’s been here, I know we couldn’t be in better hands than with Brad guiding the team going forward,” Ainge said in a statement. “I’m grateful to ownership, all of my Celtics colleagues, and the best fans in basketball for being part of the journey.”\nAinge, 62, is a franchise legend.',
]
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.*
-->
<!--
## 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: 6,433 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 16.21 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 140.69 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What type of event is being described by Pierre LeBrun in relation to the NHL</code> | <code>ESPN’s Pierre LeBrun said, “It's not just about one NHL game anymore. It's a week-long event.</code> |
| <code>Who designed the property's landscape and when was the building listed on the National Register of Historic Places</code> | <code>The property's landscape continues a circular theme, with flower beds, fencing, and parking arranged in concentric patterns around the structure. It was designed by the Washington, DC firm of Deigert & Yerkes. The building was listed on the National Register of Historic Places in 2017.</code> |
| <code>Is 'ladens' a valid word to use in Scrabble and other word games</code> | <code>Scrabble?! LADENSIs ladens valid for Scrabble? Words With Friends? Lexulous? WordFeud? Other games</code> |
* Loss: <code>selfloss.Infonce</code> with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 804 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 804 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 16.44 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 149.21 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What types of special events can the salon services be booked for</code> | <code>Our fabulous salon services are available at your special event! Whether it's a wedding, photo shoot, prom, or just a fun girls' night in- we do it all.</code> |
| <code>What material is the Hudson Baby plush hooded robe made of</code> | <code>Dimensions (Overall): 10 inches (L), 10 inches (H) x 1 inches (W)<br>Weight: 1 pounds<br>Textile Material: 100% Polyester<br>• Animal face plush hooded bath robe. • Made with 100% plush coral fleece fabric<br>• Soft and gentle on baby's skin<br>• Optimal for everyday use<br>• Affordable, high quality bath robe<br>Hudson Baby plush hooded robe is made of super soft, cozy plush material to dry and warm baby after bath or pool time.</code> |
| <code>Where is this uncommon species thought to occur</code> | <code>It is also thought to occur in New Zealand. It is an uncommon species, growing in "heathy woodland [in] semi shade".</code> |
* Loss: <code>selfloss.Infonce</code> with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 6
- `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`: 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`: 2e-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`: 6
- `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}
- `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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.9901 | 100 | 1.4311 | 0.2171 |
| 1.9802 | 200 | 0.237 | 0.1718 |
| 2.9703 | 300 | 0.1466 | 0.1561 |
| 3.9604 | 400 | 0.1084 | 0.1541 |
| 4.9505 | 500 | 0.0879 | 0.1528 |
| 5.9406 | 600 | 0.0794 | 0.1514 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.0
- Transformers: 4.48.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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",
}
```
#### Infonce
```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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