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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\n\n 1 2 \
\ 3\n 4 5 6\n 7 8 9\n\nI'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.\n\nI 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: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
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](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [reason_ccnews](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason_ccnews), [reason_reddit](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason) and [reason_s2orc](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_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](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [reason_ccnews](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason_ccnews)
- [reason_reddit](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason)
- [reason_s2orc](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason_s2orc)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 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:
```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("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]
```
<!--
### 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
#### Information Retrieval
* Datasets: `mteb/nfcorpus`, `mteb/trec-covid`, `mteb/fiqa` and `mteb/quora`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 |
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## Training Details
### Training Datasets
#### reason_ccnews
* Dataset: [reason_ccnews](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason_ccnews) at [2e4fb05](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason_ccnews/tree/2e4fb0585e862af0623b97b64d34325001b218a2)
* Size: 44,978 training samples
* Columns: <code>title</code>, <code>body</code>, and <code>reason</code>
* Approximate statistics based on the first 1000 samples:
| | title | body | reason |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.34 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 221.75 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 59.19 tokens</li><li>max: 88 tokens</li></ul> |
* Samples:
| title | body | reason |
|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Fight Leaves Wayne Simmonds Shirtless</code> | <code>Reed Saxon/AP Images<br>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.<br>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.<br>Advertisement<br>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.</code> | <code>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.</code> |
| <code>Merck CEO Kenneth Frazier ditches Trump over Charlottesville silence</code> | <code>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.<br>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.<br>“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.”<br>RELATED: At least one death has been confirmed after a car plowed into a crowd of protesters in Charlottesville<br>Trump immediately fired back at Frazier on Twitter, saying the Merck CEO now “will have...</code> | <code>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.</code> |
| <code>Lightning's Braydon Coburn: Joining road trip</code> | <code>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.<br>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.</code> | <code>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.</code> |
* Loss: [<code>ReasoningGuidedRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#reasoningguidedrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### reason_reddit
* Dataset: [reason_reddit](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason) at [2fd69ee](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason/tree/2fd69eed3d8056fbdd0c5a5e4572d2524d861626)
* Size: 41,703 training samples
* Columns: <code>title</code>, <code>body</code>, and <code>reason</code>
* Approximate statistics based on the first 1000 samples:
| | title | body | reason |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.82 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 126.63 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 59.32 tokens</li><li>max: 84 tokens</li></ul> |
* Samples:
| title | body | reason |
|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The one feature the iPad is really missing.</code> | <code>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.<br><br>I don't care if third party apps can't run in the background. I don't listen to streaming music.<br><br>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.<br><br>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.</code> | <code>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.</code> |
| <code>Dear Sydney Reddit'ers, Would you like any changes made to the style of this subreddit?</code> | <code>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. <br><br><br><br>So before I make any changes do you have any opinions or suggestions?</code> | <code>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.</code> |
| <code>I skipped bail, ran away, and never got caught. AM(A)A.</code> | <code>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.<br><br>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.<br><br>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....</code> | <code>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.</code> |
* Loss: [<code>ReasoningGuidedRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#reasoningguidedrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### reason_s2orc
* Dataset: [reason_s2orc](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason_s2orc) at [4d04170](https://huggingface.co/datasets/bwang0911/reasoning_pairs_filtered_w_reason_s2orc/tree/4d04170e1df7f9f7fc63aa92a28dddee804ef0e5)
* Size: 96,205 training samples
* Columns: <code>title</code>, <code>body</code>, and <code>reason</code>
* Approximate statistics based on the first 1000 samples:
| | title | body | reason |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 19.26 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 138.29 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 67.13 tokens</li><li>max: 107 tokens</li></ul> |
* Samples:
| title | body | reason |
|:----------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Syntheses, Structures and Properties of Two Transition Metal-Flexible Ligand Coordination Polymers</code> | <code>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.</code> | <code>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.</code> |
| <code>Discussion on the Influence and Development of Technical Aesthetics in Modern Landscape Design</code> | <code>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.</code> | <code>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.</code> |
| <code>GRIN optics for dual-band IR sensors (Conference Presentation)</code> | <code>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.</code> | <code>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.</code> |
* Loss: [<code>ReasoningGuidedRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#reasoningguidedrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `learning_rate`: 5e-06
- `num_train_epochs`: 1
- `warmup_ratio`: 0.2
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 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
</details>
### 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
```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",
}
```
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