metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:182886
  - loss:ReasoningGuidedRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: Hey Reddit, what do you do in New York City?
    sentences:
      - >-
        The second text directly answers the question posed in the first text.
        It provides personal recommendations for places to eat and things to do
        in New York City, fulfilling the user's query. The text also offers a
        specific recommendation for a restaurant, Crif Dogs, and a menu item.
      - >-
        For example, let's say you're at a section containing 9 tables

            1   2   3
            4   5   6
            7   8   9

        I'm sitting on the west side of Table 7, there are people at Tables 5
        and 6. Someone comes in through the crowd and sits on the east side of
        table 8, making awkward eye contact while we've got our mouths full.


        I always found it extremely uncomfortable... why oh why can't they just
        sit with their back to me? As far as I'm concerned this is almost as
        canonical as urinal rules.
      - >-
        This is my first year living here and I was just wondering if you knew
        of any awesome places to eat, fun places to go, trees to climb, anything
        of the sort. I for one would recommend Crif Dogs to anyone who has not
        been. Go there and get the "Spicy Redneck," you won't regret it.
  - source_sentence: 'KEYC - Charges: Man Lived With Dead Bodies of His Mother, Brother'
    sentences:
      - >-
        The second text provides a detailed elaboration of the headline. It
        specifies the location, the man's name, the charges, and the
        circumstances surrounding the discovery of the bodies. It expands on the
        initial information, providing specific details about the case.
      - >-
        Well, this is one way to go out.

        Robert Gene White took a trip to El Paso to visit the Red Parrot, a full
        service gentlemen’s club. While Mr. White was enjoying a lap dance from
        one of the lovely ladies, he passed away.

        It wasn’t until the dance was over that they noticed Mr. White wasn’t
        moving. Initially, the club thought Mr. White was “playing dead” just
        trying to get out of paying his bill. Quickly they realized he wasn’t
        faking and began CPR, then called 911. Unfortunately paramedics were
        unable to revive him.

        Is anyone else completely encapsulated at the idea that this clearly
        isn’t the first time someone has tried to “play dead” to get out of the
        bill?
      - >-
        Prosecutors say a Minnesota man lived in his house with the decomposing
        bodies of his mother and twin brother for about a year.

        Sixty-year-old Robert James Kuefler of White Bear Lake is charged with
        interference with a dead body or scene of death because he neglected to
        tell authorities they died of natural causes, according to the St. Paul
        Pioneer Press .

        The bodies were found last year. Kuefler was charged this week. He
        allegedly told police his mother, 94-year-old Evelyn Kuefler, died in
        August 2015 and his brother, Richard Kuefler, died before that and he
        couldn't bring himself to bury them.

        The complaint says his mother's body was decayed and skeletal and his
        brother's body was "mummified."

        Robert Kuefler didn't return a message left by The Associated Press.

        -KEYC News 12
  - source_sentence: Innovative procedure saves baby alpaca in Lebanon
    sentences:
      - >-
        Police say 28-year-old Wesley Flores pulled out a gun and shot himself
        in the jaw after four hours of unsuccessful negotiations. He's since
        been sent to a hospital in Lubbock.

        Authorities say Flores was originally taken into custody on a warrant
        for failing to show up to a scheduled court appearance.
      - >-
        The second text elaborates on the innovative procedure mentioned in the
        first text. It provides details about the specific case of an alpaca
        named Hercules, the innovative treatment (NuCress scaffold), the medical
        team involved, and the positive outcome of the procedure, thus expanding
        on the initial claim.
      - >-
        Hercules the alpaca was only 24 hours old when he broke his front left
        leg at Cedar Rock Ranch in Lebanon. He received a plasma transfusion and
        was bottle-fed for months. The open wound and exposed bone led to a
        serious infection, preventing the bone break from healing properly.

        The animal’s veterinarian referred him to the University of Tennessee
        College of Veterinary Medicine for advanced treatment.

        Dr. Pierre-Yves Mulon, UTCVM assistant professor in farm animal medicine
        and surgery, determined the NuCress scaffold was the best option to heal
        the fragile animal.

        The Nucress scaffold is a nanomaterial-based bone regeneration device
        pioneered by University of Arkansas at Little Rock’s systems engineering
        professor Dr. Alexadru S. Biris, UTCVM head of large animal clinical
        sciences, Dr. David Anderson and a team of designated researchers.

        The scaffold is designed to be implanted directly into the wound by a
        surgeon and can be loaded with drugs to fight infection or with hormones
        and stem cells to encourage bone growth. As a result, the scaffold can
        deliver bacteria-fighting drugs directly to the wound and be safely
        absorbed by the body, generally eliminating the need for additional
        surgeries.

        Mulon loaded the scaffold with antibiotics and implanted it into
        Hercules’ wound, expecting a long wait due to the alpaca’s condition.
        The process proved quicker than he expected.

        “Hercules responded well and fast,” said Mulon. “We was able to walk
        immediately after surgery and has been very active. The bone repaired
        within the time range expected for a closed fracture, though it was an
        open one.”

        Mulon said while other options, such as traditionally administered
        drugs, could have been used, they would have presented more obstacles
        such as future surgeries.

        “It is difficult to confirm if the results would have changed using any
        other option; however, I think it would have necessitated more time,”
        said Mulon. “Any open fracture carries a guarded to poor prognosis, and
        Hercules made it as we are very happy,”

        Researchers received a grant of more than $5 million from the Department
        of Defense and hope to develop the product for use with humans.
  - source_sentence: Trump, Macron To Hold Joint Press Conference During State Visit
    sentences:
      - >-
        Updated at 10:58 a.m. ET

        President Trump and French President Emmanuel Macron will field
        questions from reporters on Tuesday, in between talks on the Iran
        nuclear deal and a lavish state dinner.

        Macron is the first of two European leaders Trump is hosting this week.
        German Chancellor Angela Merkel will be in Washington, D.C., on Friday.
        Both France and Germany joined the U.S. in a six-nation pact with Iran
        to halt its nuclear program in exchange for sanctions relief. Trump has
        threatened to pull the U.S. out of that deal. Macron and Merkel want him
        to stay in.

        Trump's former advisers struggled to make the case for the nuclear deal,
        and the newest members of Trump's national security team are as
        skeptical of the agreement as he is.

        "People know my views on the Iran deal. It was a terrible deal. It
        should have never, ever been made," Trump said Tuesday during an Oval
        Office photo opportunity with Macron. "It's insane. It's ridiculous. It
        should have never been made, but we will be talking about it."

        Macron argues the nuclear agreement is worth preserving.

        "We have a common objective, we want to make sure there's no escalation
        and no nuclear proliferation in the region. We now need to find the
        right path forward," Macron said, through an interpreter.

        Macron has skillfully courted Trump, inviting the U.S. president to be
        his guest last year at an elaborate military parade marking Bastille Day
        in Paris. Trump was so impressed, he ordered his own military parade
        this November, marking the 100th anniversary of the end of World War I.

        The two presidents and their wives celebrated the wartime alliance
        between the U.S. and France on Monday by planting an oak tree on the
        South Lawn of the White House. The sapling comes from Belleau Wood,
        where more than 9,000 Marines died in the final months of the first
        world war, according to a White House statement.

        Later, the two couples took a sightseeing helicopter tour of Washington,
        then held a private dinner at George Washington's historic Mt. Vernon
        estate.

        Despite their evident personal chemistry, Trump and Macron have
        significant policy differences to discuss. In addition to the Iran
        nuclear deal, Macron wants a permanent exemption from the president's
        new steel and aluminum tariffs. And he'd like to see a more lasting
        commitment from the U.S. to stabilization efforts in Syria. Military
        forces from France and the U.K. joined the U.S. in launching air strikes
        on Syria earlier this month in retaliation for a suspected chemical
        weapons attack. But Trump is impatient to withdraw U.S. troops from that
        country as quickly as possible.

        "What you do have are two leaders who have a great deal of respect for
        one another, who have a great friendship," said White House spokeswoman
        Sarah Sanders. She added that friendship allows the two men to have
        "very open and candid conversations."

        Sanders said she expects "a very productive and very positive state
        visit for both countries."

        The visit will be marked by the first state dinner of the Trump
        administration. The White House has been decorated for the event with
        cherry blossoms, sweet peas and white lilacs. The menu is American with
        French influences: spring lamb and jambalaya.

        On Wednesday, Macron is set to address a joint session of Congress.
      - >-
        Liverpool manager Jurgen Klopp admits that he cannot explain his side's
        performance during their 2-2 draw with Sunderland at the Stadium of
        Light.

        Liverpool manager Jurgen Klopp has admitted that he cannot explain his
        side's performance during the 2-2 draw with Sunderland at the Stadium of
        Light this afternoon.

        The Reds led twice through goals from Daniel Sturridge and Sadio Mane,
        but on both occasions they were pegged back by penalties from Jermain
        Defoe.

        Liverpool had been looking for five straight league wins for the first
        time under Klopp, but the German suggested that the two-day turnaround
        between matches prevented them from playing their best football.

        "I am not able to explain it because I don't know exactly what I saw, my
        team were fighting but I wasn't sure if they could do it. We can play
        better football but I'm not sure if you can play better with that
        break," he told BBC Sport.

        "I don't know how it feels when you have to do the things you have to do
        today. I told the players if nobody wanted to play I would never speak
        about and not tell anyone, but nobody came and that was a good thing.
        About the football we played, I actually have no idea how to speak about
        it.

        "There was no foul before the free kick for the second penalty. You need
        a little bit of luck, but Sunderland worked hard too and maybe they
        deserved it."

        The results means that Liverpool miss the chance to close the gap on
        Premier League leaders Chelsea to three points.
      - >-
        The second text elaborates on the title by providing details about the
        joint press conference, including the date, topics to be discussed (Iran
        nuclear deal, tariffs, Syria), and the context of the state visit. It
        also mentions the leaders' differing views and the overall atmosphere of
        the visit.
  - source_sentence: Crossover and multicriticality due to the Dzyaloshinsky-Moriya interaction
    sentences:
      - >-
        Attention is focused on the theoretical principles governing the
        underlying geometry of motifs, border patterns and all-over patterns.
        The systematic classification and construction of two-dimensional
        periodic patterns and tilings is introduced, with particular relerence
        to two-colour and higher colour counterchange possibilities. An
        identification is made of the geometrical restraints encountered when
        introducing systematic interchange of colour. A wide ranging series of
        original patterns and tilings is constructed and fully illustrated;
        these designs have been printed in fabric form and are presented in the
        accompanying exhibition.
      - >-
        We show that the addition of a Dzyaloshinsky-Moriya interaction to a
        Heisenberg ferromagnet introduces only one crossover exponent, which is
        the same as for the usual uniaxial anisotropy. This result is in
        contrast to a previous report by Liu.
      - >-
        The second text elaborates on the first by specifying the impact of the
        Dzyaloshinsky-Moriya interaction on a Heisenberg ferromagnet. It
        highlights a key finding: the introduction of only one crossover
        exponent, contrasting with a prior study. This directly addresses the
        topic introduced in the title.
datasets:
  - bwang0911/reasoning_pairs_filtered_w_reason_ccnews
  - bwang0911/reasoning_pairs_filtered_w_reason
  - bwang0911/reasoning_pairs_filtered_w_reason_s2orc
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on BAAI/bge-base-en-v1.5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: mteb/nfcorpus
          type: mteb/nfcorpus
        metrics:
          - type: cosine_accuracy@1
            value: 0.5046439628482973
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6346749226006192
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6965944272445821
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7678018575851393
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5046439628482973
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3993808049535604
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.3572755417956657
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.28668730650154794
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.06516889989501519
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.11387269263353653
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.1396374157566347
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.18692123966555005
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.38253279961982706
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5874551575015973
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.195968677576039
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: mteb/trec covid
          type: mteb/trec-covid
        metrics:
          - type: cosine_accuracy@1
            value: 0.86
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.86
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.8799999999999999
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.856
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.8320000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.0007006541633990996
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.002166976340027841
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.003562871514029663
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.00692643022454112
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.843458611785082
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9233333333333333
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5214168404644098
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: mteb/fiqa
          type: mteb/fiqa
        metrics:
          - type: cosine_accuracy@1
            value: 0.35802469135802467
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5231481481481481
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5848765432098766
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6743827160493827
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.35802469135802467
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.23251028806584362
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16944444444444445
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10648148148148148
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.18514227970246488
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.31801450435709694
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3720212443592073
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.45586599186136223
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3826690717843391
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4577338085439937
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.32368570015506426
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: mteb/quora
          type: mteb/quora
        metrics:
          - type: cosine_accuracy@1
            value: 0.8112
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9258
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9553
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9773
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8112
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3723666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.24552000000000013
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.13407000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7047405405718852
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8691192994653526
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9144622696502942
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9524565789137283
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8811914153543994
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8729545634920601
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8501811476426027
            name: Cosine Map@100

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the reason_ccnews, reason_reddit and reason_s2orc datasets. 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
Maximum Sequence Length: 256 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Datasets:

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bwang0911/reasoning-bge")
# Run inference
sentences = [
    'Crossover and multicriticality due to the Dzyaloshinsky-Moriya interaction',
    'We show that the addition of a Dzyaloshinsky-Moriya interaction to a Heisenberg ferromagnet introduces only one crossover exponent, which is the same as for the usual uniaxial anisotropy. This result is in contrast to a previous report by Liu.',
    'The second text elaborates on the first by specifying the impact of the Dzyaloshinsky-Moriya interaction on a Heisenberg ferromagnet. It highlights a key finding: the introduction of only one crossover exponent, contrasting with a prior study. This directly addresses the topic introduced in the title.',
]
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]

Evaluation

Metrics

Information Retrieval

Datasets: mteb/nfcorpus, mteb/trec-covid, mteb/fiqa and mteb/quora
Evaluated with InformationRetrievalEvaluator

Metric	mteb/nfcorpus	mteb/trec-covid	mteb/fiqa	mteb/quora
cosine_accuracy@1	0.5046	0.86	0.358	0.8112
cosine_accuracy@3	0.6347	1.0	0.5231	0.9258
cosine_accuracy@5	0.6966	1.0	0.5849	0.9553
cosine_accuracy@10	0.7678	1.0	0.6744	0.9773
cosine_precision@1	0.5046	0.86	0.358	0.8112
cosine_precision@3	0.3994	0.88	0.2325	0.3724
cosine_precision@5	0.3573	0.856	0.1694	0.2455
cosine_precision@10	0.2867	0.832	0.1065	0.1341
cosine_recall@1	0.0652	0.0007	0.1851	0.7047
cosine_recall@3	0.1139	0.0022	0.318	0.8691
cosine_recall@5	0.1396	0.0036	0.372	0.9145
cosine_recall@10	0.1869	0.0069	0.4559	0.9525
cosine_ndcg@10	0.3825	0.8435	0.3827	0.8812
cosine_mrr@10	0.5875	0.9233	0.4577	0.873
cosine_map@100	0.196	0.5214	0.3237	0.8502

Training Details

Training Datasets

reason_ccnews

Dataset: reason_ccnews at 2e4fb05
Size: 44,978 training samples
Columns: title, body, and reason

Approximate statistics based on the first 1000 samples:

	title	body	reason
type	string	string	string
details	min: 6 tokens mean: 15.34 tokens max: 42 tokens	min: 21 tokens mean: 221.75 tokens max: 256 tokens	min: 28 tokens mean: 59.19 tokens max: 88 tokens

Samples:

title	body	reason
`Fight Leaves Wayne Simmonds Shirtless`	Reed Saxon/AP Images Kevin Bieksa and Wayne Simmonds dropped the gloves just 95 seconds into last night’s 4-3 Ducks shootout win over the Flyers, and Bieksa immediately yanked his opponent’s jersey over his head, to the delight of the crowd and to grins from Simmonds and the officials. That’s not supposed to happen. NHL players wear something called a fight strap, which binds the back of the jersey to the pants, preventing the jersey from being pulled off. (Losing a jersey is an advantage in a fight, as it gives the shirtless player’s opponent nothing to grab on to. Sabres enforcer Rob Ray was notorious for losing his gear in a fight, occasionally taking it off himself before clinching.) Any player who engaged in a fight without wearing a fight strap is subject to an automatic game misconduct. Advertisement Simmonds wasn’t ejected, though; at the one-minute mark of the video above, you can see he did have his fight strap properly attached. It just broke, which happens on occasion.	`The article describes a hockey fight involving Wayne Simmonds, confirming the title's claim. It details the fight, including Simmonds' jersey being pulled off, and explains the rules and context around the incident, directly elaborating on the event suggested by the title.`
`Merck CEO Kenneth Frazier ditches Trump over Charlottesville silence`	Merck CEO Kenneth C. Frazier resigned from the president’s council on manufacturing Monday in direct protest of President Donald Trump’s lack of condemnation of white nationalist actions in Charlottesville, Va. over the weekend. In a statement, Frazier, who is African-American, said he believes the country’s strength comes from the diversity of its citizens and that he feels personally compelled to stand up for that diversity and against intolerance. “America’s leaders must honor our fundamental values by clearly rejecting expressions of hatred, bigotry and group supremacy, which run counter to the American ideal that all people are created equal,” he wrote. “As CEO of Merck, and as a matter of personal conscience, I feel a responsibility to take a stand against intolerance and extremism.” RELATED: At least one death has been confirmed after a car plowed into a crowd of protesters in Charlottesville Trump immediately fired back at Frazier on Twitter, saying the Merck CEO now “will have...	`The second text provides a detailed elaboration of the first. It explains the context of Kenneth Frazier's resignation, the reasons behind it (Trump's silence on Charlottesville), and includes Frazier's statement. It also provides additional background information about Frazier and the President's Manufacturing Council.`
`Lightning's Braydon Coburn: Joining road trip`	Coburn (lower body) will travel with the team on its upcoming four-game road trip and is hoping to play at some point in the second half of the trip, Bryan Burns of the Lightning's official site reports. The veteran blueliner is yet to play in the month of December, having already missed four games. However, the fact that Coburn is traveling with the team and has been given a chance to play at some point within the next week will be music to the ears of fantasy owners who benefited from Coburn's surprising production -- seven points in 25 games -- earlier in the season. Keep an eye out for updates as the trip progresses.	`The second text elaborates on the first by providing details about Braydon Coburn's situation. It specifies that he will join the team on a road trip and offers context about his injury, recovery timeline, and potential for playing, directly expanding on the initial announcement.`

Loss: ReasoningGuidedRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

reason_reddit

Dataset: reason_reddit at 2fd69ee
Size: 41,703 training samples
Columns: title, body, and reason

Approximate statistics based on the first 1000 samples:

	title	body	reason
type	string	string	string
details	min: 6 tokens mean: 18.82 tokens max: 69 tokens	min: 16 tokens mean: 126.63 tokens max: 256 tokens	min: 42 tokens mean: 59.32 tokens max: 84 tokens

Samples:

title	body	reason
`The one feature the iPad is really missing.`	I don't care about the lack of camera. I never use the one on my MacBook, and even if I did the angle would be terrible on the iPad. I don't care if third party apps can't run in the background. I don't listen to streaming music. I don't care that the App Store is a closed system. I can jailbreak for myself and I think the closed system works better for most users. The one feature I want is User Accounts and a Guest Account. If this device is meant to be a coffee table computer, it needs to be able to accomadate multiple users.	`The second text identifies the missing feature from the iPad as user accounts and a guest account. The first sentence in the second text sets up a contrast by stating what the author doesn't care about. The final sentence directly addresses the prompt by stating the feature the author does want.`
`Dear Sydney Reddit'ers, Would you like any changes made to the style of this subreddit?`	`I was going to subtly edit the style of the Sydney subreddit but then I found this post and realised that people have very strong opinions about how their reddit should look. So before I make any changes do you have any opinions or suggestions?`	`The second text directly responds to the question in the first text. It acknowledges the query about subreddit style changes and seeks further input from the community before making any modifications. It demonstrates an understanding of the original post's intent and a willingness to engage with user preferences.`
`I skipped bail, ran away, and never got caught. AM(A)A.`	Long/short story, I went to work in the United States in the last 90s and was busted in a major drug raid. I risked up to lifetime in jail if caught since I was associated with so many crimes; at the bare minimum, said my attorney, I was looking at 7 years in jail, and much more likely more than this. My attorney said I was in a lot of trouble. He was the first to bring it up. I did not want to lose 10, 15 or 25 years of my life in jail, especially at my age. Since I was not a United States citizen, I should simply skip bail and run away. And never come back. My bail was initially supposed to be $300,000 but my attorney managed to get the judge to set a final bail of $100,000. He explained I was a trustworthy person, lawfully employed, who never did anything wrong and never committed any crime. He portrayed me as someone trustworthy and intelligent who could take care of his responsibilities. The judge agreed and decided on a very low bail, especially for the crimes I was accused of....	`The second text provides a detailed account of the events summarized in the first text. It elaborates on the circumstances of skipping bail, running away, and avoiding capture, offering specific details about the legal situation, the escape plan, and the aftermath. The AMAA at the end indicates the user is open to questions about the story.`

Loss: ReasoningGuidedRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

reason_s2orc

Dataset: reason_s2orc at 4d04170
Size: 96,205 training samples
Columns: title, body, and reason

Approximate statistics based on the first 1000 samples:

	title	body	reason
type	string	string	string
details	min: 6 tokens mean: 19.26 tokens max: 75 tokens	min: 17 tokens mean: 138.29 tokens max: 256 tokens	min: 47 tokens mean: 67.13 tokens max: 107 tokens

Samples:

title	body	reason
`Syntheses, Structures and Properties of Two Transition Metal-Flexible Ligand Coordination Polymers`	Two coordination polymers based on 3,5-bis(4-carboxyphenylmethyloxy) benzoic acid (H3L), [M(HL)]·2H2O M = Mn(1), Co(2), have been synthesized under hydrothermal conditions. Their structures have been determined by single-crystal X-ray diffraction and further characterized by elemental analysis, IR spectra and TGA. The two complexes possess 3D framework with diamond channels resulting from the trans-configuration of the flexible ligand and three coordination modes, 3(η2, η1), 2(η1, η1), η1, of carboxyl groups in the ligand. The framework can be represented with Schlafli symbol of (48·66)(47·66). The wall of the channel consists of left- or right-handed helical polymeric chains. UV–visible–NIR and photoluminescence spectra, magnetic properties of 1 and 2 have also been discussed.	`The second text elaborates on the title by detailing the synthesis, structure, and properties of two specific transition metal coordination polymers. It provides the chemical formula, synthesis method, structural characteristics (3D framework, channels), and characterization techniques (X-ray diffraction, IR spectra, etc.) mentioned in the title.`
`Discussion on the Influence and Development of Technical Aesthetics in Modern Landscape Design`	`The source of technical aesthetics was introduced and its meaning was explained.The relations between technical aesthetics and modern landscpae design were discussed.The embodiment of technical aesthetics in landscpae design was discussed in the aspects of new material,new technology,new structureand new apparatus.It was put forward that the the development direction of technical aesthetics were tending to sensibility, native land and zoology.`	`The second text directly addresses the topic introduced in the first text. It explores the meaning, application, and future directions of technical aesthetics within modern landscape design, elaborating on the influence and development mentioned in the title.`
`GRIN optics for dual-band IR sensors (Conference Presentation)`	Graded index (GRIN) optics offer potential for both weight savings and increased performance but have until recently been limited to visible and NIR bands (wavelengths shorter than about 0.9 µm). NRL has developed glass-based IR-GRIN lenses compatible with SWIR-LWIR wavebands. Recent designs show the potential for significant SWaP reduction benefits and improved performance using IR-GRIN lens elements in dual-band, MWIR-LWIR sensors. The SWaP and performance advantages of IR-GRIN lenses in platform-relevant dual-band imagers will be presented.	`The second text elaborates on the first by providing a detailed description of GRIN optics, specifically for dual-band IR sensors. It explains the potential benefits (weight savings, increased performance) and highlights the development of IR-GRIN lenses compatible with SWIR-LWIR wavebands, aligning directly with the conference presentation topic.`

Loss: ReasoningGuidedRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
learning_rate: 5e-06
num_train_epochs: 1
warmup_ratio: 0.2
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: 8
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-06
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.2
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

Training Logs

Epoch	Step	Training Loss	mteb/nfcorpus_cosine_ndcg@10	mteb/trec-covid_cosine_ndcg@10	mteb/fiqa_cosine_ndcg@10	mteb/quora_cosine_ndcg@10
-1	-1	-	0.3714	0.8385	0.3831	0.8889
0.0070	10	0.9492	-	-	-	-
0.0140	20	0.9799	-	-	-	-
0.0210	30	0.84	-	-	-	-
0.0280	40	0.9555	-	-	-	-
0.0350	50	0.9292	0.3695	0.8401	0.3840	0.8892
0.0420	60	1.1549	-	-	-	-
0.0490	70	0.8573	-	-	-	-
0.0559	80	0.5784	-	-	-	-
0.0629	90	0.7275	-	-	-	-
0.0699	100	0.4792	0.3766	0.8457	0.3886	0.8887
0.0769	110	0.6293	-	-	-	-
0.0839	120	0.5167	-	-	-	-
0.0909	130	0.3838	-	-	-	-
0.0979	140	0.3458	-	-	-	-
0.1049	150	0.4897	0.3739	0.8494	0.3866	0.8876
0.1119	160	0.3124	-	-	-	-
0.1189	170	0.4367	-	-	-	-
0.1259	180	0.3565	-	-	-	-
0.1329	190	0.2646	-	-	-	-
0.1399	200	0.2	0.3757	0.8508	0.3852	0.8860
0.1469	210	0.2051	-	-	-	-
0.1538	220	0.1248	-	-	-	-
0.1608	230	0.2398	-	-	-	-
0.1678	240	0.1599	-	-	-	-
0.1748	250	0.3251	0.3743	0.8527	0.3840	0.8840
0.1818	260	0.263	-	-	-	-
0.1888	270	0.2523	-	-	-	-
0.1958	280	0.2156	-	-	-	-
0.2028	290	0.1587	-	-	-	-
0.2098	300	0.1977	0.3777	0.8557	0.3859	0.8830
0.2168	310	0.1544	-	-	-	-
0.2238	320	0.1301	-	-	-	-
0.2308	330	0.1178	-	-	-	-
0.2378	340	0.1084	-	-	-	-
0.2448	350	0.1784	0.3800	0.8540	0.3860	0.8821
0.2517	360	0.1541	-	-	-	-
0.2587	370	0.0982	-	-	-	-
0.2657	380	0.1897	-	-	-	-
0.2727	390	0.117	-	-	-	-
0.2797	400	0.1806	0.3785	0.8458	0.3861	0.8818
0.2867	410	0.1258	-	-	-	-
0.2937	420	0.1249	-	-	-	-
0.3007	430	0.1987	-	-	-	-
0.3077	440	0.1512	-	-	-	-
0.3147	450	0.1646	0.3817	0.8422	0.3829	0.8814
0.3217	460	0.1322	-	-	-	-
0.3287	470	0.1464	-	-	-	-
0.3357	480	0.1488	-	-	-	-
0.3427	490	0.1033	-	-	-	-
0.3497	500	0.1209	0.3825	0.8435	0.3827	0.8812

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.5.0.dev0
Transformers: 4.50.0
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 2.21.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@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",
}