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Add new SentenceTransformer model
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metadata
base_model: sentence-transformers/all-mpnet-base-v2
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:5166
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Question: Who is the dungeon master in the Knights of the Arcade comedy
      show, and how are the destinations and battles decided during the
      performance?
    sentences:
      - >-
        Event Name: Knights of the Arcade: Epic D&D Adventure

        Categories: Entertainment, Nightlife

        Dates: Jun 29, 2024 - Jun 29, 2024   | 9:00 pm - 10:30 pm

        Location: Arcade Comedy Theater, 943 Liberty Ave, Pittsburgh, PA 15222

        Description: “Best Nerd Fantasy Come to Life” by Pittsburgh Magazine“A
        neo-geek wet dream”  Pittsburgh City PaperA comedy quest awaits!
        Knights of the Arcade is an award-winning comedy show that takes
        audiences on a wild, madcap adventure every month. A recurring cast of
        characters (a dwarf, a monk, a rogue, a sorcerer and a fighter) are
        joined by special guests and led by their maniacal dungeon master. Where
        they’re going, who they fight, and if they ultimately succeed is decided
        upon live dice that are rolled and projected on the theater wall.
      - >-
        The Pirates are also often referred to as the "Bucs" or the "Buccos"
        (derived from buccaneer, a synonym for pirate). Since 2001 the team has
        played its home games at PNC Park, a 39,000-seat stadium along the
        Allegheny River in Pittsburgh's North Side. The Pirates previously
        played at Forbes Field from 1909 to 1970 and at Three Rivers Stadium
        from 1970 to 2000. Since 1948 the Pirates' colors have been black, gold
        and white, derived from the flag of Pittsburgh and matching the other
        major professional sports teams in Pittsburgh, the Steelers and the
        Penguins.The Pittsburgh Pirates are an American professional baseball
        team based in Pittsburgh. The Pirates compete in Major League Baseball
        (MLB) as a member club of the National League (NL) Central Division.
        Founded as part of the American Association in 1881 under the name
        Pittsburgh Alleghenys, the club joined the National League in 1887 and
        was a member of the National League East from 1969 through 1993. The
        Pirates have won five World
      - |-
        STEELERS IN THE POSTSEASON (36-30)
        Year Record Game Date Opponent Attendance Steelers Opponent Result
        2015 10-6 AFC Wild Card Game 01/09/2016 at Cincinnati 63,257 18 16 W
        AFC Divisional Playoff 01/17/2016 at Denver 79,956 16 23 L
        2016# 11-5 AFC Wild Card Game 01/08/2017 Miami 66,726 30 12 W
        AFC Divisional Playoff 01/15/2017 at Kansas City 75,678 18 16 W
        AFC Championship Game 01/22/2017 at New England 66,829 36 17 L
        2017# 13-3 AFC Divisional Playoff 01/14/2018 Jacksonville 64,524 42 45 L
        2020# 12-4 AFC Wild Card Game 01/03/2021 Cleveland - 37 48 L
        2021 9-7-1 AFC Wild Card Game 01/16/2022 at Kansas City 73,253 21 42 L
        2023 10-7 AFC Wild Card Game 01/15/202 4 at Buffalo 70,040 17 31 L
        *AFC Central Champion
        #AFC North Champion
        +AFC ChampionSTEELERS IN THE POSTSEASON
         2023 PITTSBURGH STEELERS
         421
         STEELERS IN THE POSTSEASON
  - source_sentence: 'Question: What is the Local Services Tax and how is it collected?'
    sentences:
      - >-
        the 1916 Centennial of Pittsburgh's 1816 incorporation as a City. At
        the                                          March 1916 dedication
        ceremony, Mayor Joseph Armstrong placed a time capsule into the still
        under construction building. Two and a half
        years                                          later in December 1917,
        he would become the first Mayor to call the City-County Building a
        second home. The missing time capsule has yet to
        be                                          discovered.
      - >-
        The first City Hall at Market Square.

        The second City Hall on Smithfield Street.

        Mayor David Lawrence strikes the first blow for the demolition of the
        second City Hall.
      - >-
        EXEMPT P ERSON – a person who files an exemption certificate with his
        employer affirming  

        that he reasonably expects to receive earned income and net profits from
        all sources within the 

        City of less than twelve thousand dollars ($12,000) in the calendar year
        for wh ich the exemption 

        certificate is filed. See Section 301(h) below, and Section 2 of the
        Local Tax Enabling Act, 53 P.S. § 

        6924.301.1, for other  exemptions.  

        INCOME  all earned income and net profits from whatever source derived,
        including but not 

        limited to  salaries, wages, bonuses, commissions and income from self
        -employment earned in 

        Pittsburgh.  

        LOCAL SERVICES TAX (LST)  a tax on individuals for the privilege of
        engaging in an 

        occupation. The Local Services Tax may be levied, assessed and collected
        by the  political 

        subdivision of the taxpayer’s primary place of employment.  

        OCCUPATION  any livelihood, job, trade, profession, business or
        enterprise of any kind for
  - source_sentence: >-
      "What is the nature of the incident being investigated by Zone Five
      Officers in Homewood on April 23, 2024?"
    sentences:
      - >-
        Event Name: Saturday Night Improv @ BGC!

        Date: Saturdays, 7:30-9:30 p.m.

        Location: BGC Community Activity Center: 113 N. Pacific Ave., Pittsburgh
        | Garfield

        Price Information: GET TICKETS: 10

        Categories: Comedy, Theater

        Description: It's time to Love, Laugh and Enjoy. Join us at the BGC
        Activity Center Saturday evenings for an evening of improv with
        performances by Narsh and Penny Pressed! Shows start promptly at 7:30 PM
        so don't be late! 412-441-6950


        Event Name: Swing City

        Date: Saturdays, 8 p.m.

        Location: Wightman School: 5604 Solway, Pittsburgh | Squirrel Hill

        Categories: Other Stuff

        Description: Learn & practice swing dancing skills w/ the Jim Adler
        Band. 412-759-1569
      - >-
        Police Investigate Stabbing Incident in Beltzhoover - 04.23.2024

        Zone Five Officers Investigate Homewood Shooting Incident - 04.23.2024

        Violent Crimes Division VCU Detectives Make Firearms Arrest in Spring
        Garden - 04.19.2024

        UPDATE: Detectives Seek Assistance in Search for Missing 12-Year-Old
        Girl - 04.19.2024

        UPDATE: Police Investigate Aggravated Assault on Riverwalk in Point
        State Park - 04.19.2024

        Police Investigate Homicide Inside Larimer Residence  - 04.19.2024

        UPDATE: Police Seek the Public's Help in Locating Missing Juvenile Male
        - 04.19.2024

        UPDATE: Pittsburgh Police Ask for Public's Help to Find Missing Woman -
        04.15.2024

        Police Investigate Shooting Incident in Allegheny Center  - 04.13.2024

        UPDATE: Pittsburgh Public Safety Responds to Barge Emergency on Ohio
        River - 04.12.2024

        Police Make Ethnic Intimidation and Criminal Mischief Arrest in Squirrel
        Hill  - 04.12.2024

        UPDATE: Police Seek the Public's Assistance in Locating Missing Boy -
        04.11.2024
      - >-
        24
         
        $ (Millions)Select Major Expenditures, 2018-2022

        2018 2019 2020

        2021 2022Health Insurance

        Workers' CompensationPension and OPEBDebt
        Service050,000,000100,000,000150,000,000

        Health Insurance

        These expenditures are categorized within the Personnel  Employment
        Benefits subclass. Prior to 2016 these 

        expenditures were budgeted centrally in the Department of Human
        Resources and Civil Service. Except for retiree 

        health insurance, these expenditures are budgeted across all divisions
        based on staffing levels and plan 

        elections.
         Health Insurance
        52101  Health Insurance

        52111  Other Insurance and Benefits

        52121  Retiree Health Insurance

        Workers’ Compensation

        These expenditures are categorized within the Personnel  Employment
        Benefits subclass. Most medical, 

        indemnity, and fees are budgeted across divisions with outstanding
        claims. Legal and settlement expenses 

        remain budgeted in the Department of Human Resources and Civil Service
        with accounts organized as follows:
  - source_sentence: >-
      Answer: The passage does not provide information about the longest
      reception for the Steelers in the Wild Card Game against Cincinnati.
    sentences:
      - >-
        09/08 Lions RESERVE/LEAGUE SUSP. T 27-27 +

        09/15 at Ravens RESERVE/LEAGUE SUSP. L 17-23

        09/22 Panthers RESERVE/LEAGUE SUSP. L 20-38

        09/29 Seahawks RESERVE/LEAGUE SUSP. L 10-27

        10/06 at Bengals RESERVE/LEAGUE SUSP. W 26-23

        10/13 Falcons RESERVE/LEAGUE SUSP. W 34-33

        10/20 at Giants S 7701.0 13.0 0 0 1 0 0 0 0 0 1 0 0 0 0000 000 W 27-21

        10/27 at Saints S 6510.0 0.0 0 0 0 1 0 0 0 1 0 0 0 0 0000 000 L 9-31

        10/31 49ers S 3210.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0000 000 L 25-28

        11/10 at Buccaneers S 3300.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0000 000 L
        27-30

        11/17 at 49ers S 4400.0 0.0 0 0 0 0 0 0 0 1 0 0 0 0 0000 000 L 26-36

        12/01 Rams S 8530.0 0.0 1 10 0 0 0 0 0 0 0 0 0 0 0000 000 L 7-34

        12/08 Steelers S 5410.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0000 000 L 17-23

        12/15 Browns S 7700.0 0.0 0 0 0 1 0 0 0 3 0 0 0 0 0000 000 W 38-24

        12/22 at Seahawks S 3300.0 0.0 1 18 0 0 0 0 0 0 0 0 0 0 0000 000 W 27-13

        12/29 at Rams S 7610.0 0.0 1 1 0 0 0 0 0 2 0 0 0 0 0000 000 L 24-31
      - >-
        Program 

         Clinical field education to emergency medicine physician residents in
        the University of Pittsburgh 

        Emergency Medicine Residency program 
         
        2023 Accomplishments
         
         Financial Accomplishments:

         Income from transports increased by $1.8M from same time period last
        year

         Bureau slated to bring in an additional $5M in revenue for 2023

         Personnel Accomplishments:

         6 new River Rescue Divers went through intensive training and all
        successfully completed the 

        class

         Increase in promotions to upper administration

         Employee Safety Initiatives: 

         Implementation of Cordico App for employee wellness

         Access control security system installed in all EMS facilities 

         Equipment Initiatives:

         Bureau was approved to receive state of the art mannequins to simulate
        real life patients during 

        emergencies

         Billing company to purchase equipment/medication dispensary machines
        to be located in 5 areas
      - >-
        Pittsburgh 31

        Cincinnati 17

        CINCINNATI   Pittsburgh scored 24 unanswered points to turn a 17-7
        deficit into a 

        31-17 victory over Cincinnati in the AFC Wild Card Game at Paul Brown
        Stadium. 

        The Pittsburgh offense compiled 346 total yards led by QB Ben
        Roethlisberger, who 

        tossed three touchdowns and finished with a QB rating of 148.7. RB
        Jerome Bettis ran for 52 

        yards on 10 carries (5.2 avg.) and one touchdown. WR Cedrick Wilson
        caught three passes 

        for 104 yards (34.7 avg.), with one touchdown. 

        The Steelers defense recorded four sacks and two interceptions while
        holding the 

        Bengals to just 84 yards rushing. 

        Cincinnati was dealt an early blow when starting QB Carson Palmer
        suffered a torn 

        ACL on the first offensive play of the game. The Bengals jumped out to a
        10-0 lead with a 

        23-yard field goal by K Shayne Graham and a 20-yard touchdown run by RB
        Rudi Johnson.

        Pittsburgh got on the board when RB Willie Parker took a screen pass 19
        yards for a
  - source_sentence: >-
      "What cultural celebration will be honored at the 2024 Greater Pittsburgh
      Lunar New Year Gala, and what is the significance of this event in the
      community?"
    sentences:
      - >-
        This page informs City of Pittsburgh residents about the city's Snow
        Angels program. This page is also where volunteers can sign up, and
        recipients can submit a request.

        City Collection Equity Audit

        The City of Pittsburgh is conducting an audit to identify inequity and
        bias in the City’s collection of public art and memorials.

        Davis Avenue Bridge

        Design and construction for the new Davis Avenue Bridge between Brighton
        Heights and Riverview Park.

        South Side Park Public Art

        A new public art project is being planned in South Side Park. This is
        being done in coordination with the park’s Phase 1 renovations and
        funded by the Percent For Art.

        Projects that are no longer accepting feedback, but are now in the
        construction or development phase.

        PHAD Projects

        Current Projects  find out about ongoing projects underway throughout
        the city and learn how to apply for new projects each year.

        Emerald View Phase I Trails & Trailheads
      - >-
        of Pittsburgh and greater southwestern Pennsylvania. Justin is employed
        within the Cultural Resources practice of Michael Baker International.
        He is Director Emeritus of Preservation Pittsburgh and a past president
        of the East Liberty Valley Historical Society. Justin is a graduate of
        the University of Pittsburgh (B.A. Architectural Studies, 2008) and
        Columbia University (M.S. Historic Preservation, 2010).Todd Wilson, MBA,
        PE, is an award-winning transportation engineer, named one of Pittsburgh
        Business Times’ 20 Engineers to Know in 2022. He has co-authored two
        books on Pittsburgh’s bridges,Images of America Pittsburgh’s Bridges and
        Engineering Pittsburgh a History of Roads, Rails, Canals, Bridges, and
        More.An engineering graduate of Carnegie Mellon, Todd has extensive
        knowledge on bridges, having photographed them in all 50 states and 25
        countries, and he has presented at many conferences. Check out his
        Pittsburgh bridge photography on Instagram @pghbridges.TOUR
        STARTS/ENDS:Gateway
      - |-
        Event Name: 2024 Greater Pittsburgh Lunar New Year Gala
        Categories: Arts + Culture, Community, Holidays, Nightlife
        Dates: Feb 3, 2024 - Feb 3, 2024   | 4:00 pm - 9:00 pm
        Location: PNC Theater, 350 Forbes Avenue, Pittsburgh, PA 15222
model-index:
  - name: MPNet base trained on synthetic Pittsburgh data
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: pittsburgh
          type: pittsburgh
        metrics:
          - type: cosine_accuracy@1
            value: 0.7375145180023229
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9037940379403794
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9368950832365467
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9628339140534262
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7375145180023229
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.30126467931345985
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1873790166473093
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09628339140534262
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7375145180023229
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9037940379403794
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9368950832365467
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9628339140534262
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8590408201907759
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.824762258110111
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8263189855192845
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.7375145180023229
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9037940379403794
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9368950832365467
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9628339140534262
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.7375145180023229
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.30126467931345985
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.1873790166473093
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09628339140534262
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.7375145180023229
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.9037940379403794
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.9368950832365467
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9628339140534262
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8590408201907759
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.824762258110111
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.8263189855192845
            name: Dot Map@100

MPNet base trained on synthetic Pittsburgh data

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2
  • Maximum Sequence Length: 384 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, '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})
  (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("lizchu414/mpnet-base-all-pittsburgh-squad")
# Run inference
sentences = [
    '"What cultural celebration will be honored at the 2024 Greater Pittsburgh Lunar New Year Gala, and what is the significance of this event in the community?"',
    'Event Name: 2024 Greater Pittsburgh Lunar New Year Gala\nCategories: Arts + Culture, Community, Holidays, Nightlife\nDates: Feb 3, 2024 - Feb 3, 2024   | 4:00 pm - 9:00 pm\nLocation: PNC Theater, 350 Forbes Avenue, Pittsburgh, PA 15222',
    "This page informs City of Pittsburgh residents about the city's Snow Angels program. This page is also where volunteers can sign up, and recipients can submit a request.\nCity Collection Equity Audit\nThe City of Pittsburgh is conducting an audit to identify inequity and bias in the City’s collection of public art and memorials.\nDavis Avenue Bridge\nDesign and construction for the new Davis Avenue Bridge between Brighton Heights and Riverview Park.\nSouth Side Park Public Art\nA new public art project is being planned in South Side Park. This is being done in coordination with the park’s Phase 1 renovations and funded by the Percent For Art.\nProjects that are no longer accepting feedback, but are now in the construction or development phase.\nPHAD Projects\nCurrent Projects – find out about ongoing projects underway throughout the city and learn how to apply for new projects each year.\nEmerald View Phase I Trails & Trailheads",
]
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

Metric Value
cosine_accuracy@1 0.7375
cosine_accuracy@3 0.9038
cosine_accuracy@5 0.9369
cosine_accuracy@10 0.9628
cosine_precision@1 0.7375
cosine_precision@3 0.3013
cosine_precision@5 0.1874
cosine_precision@10 0.0963
cosine_recall@1 0.7375
cosine_recall@3 0.9038
cosine_recall@5 0.9369
cosine_recall@10 0.9628
cosine_ndcg@10 0.859
cosine_mrr@10 0.8248
cosine_map@100 0.8263
dot_accuracy@1 0.7375
dot_accuracy@3 0.9038
dot_accuracy@5 0.9369
dot_accuracy@10 0.9628
dot_precision@1 0.7375
dot_precision@3 0.3013
dot_precision@5 0.1874
dot_precision@10 0.0963
dot_recall@1 0.7375
dot_recall@3 0.9038
dot_recall@5 0.9369
dot_recall@10 0.9628
dot_ndcg@10 0.859
dot_mrr@10 0.8248
dot_map@100 0.8263

Training Details

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_eval_batch_size: 2
  • eval_accumulation_steps: 1
  • learning_rate: 2e-05
  • warmup_ratio: 0.1
  • 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: 8
  • per_device_eval_batch_size: 2
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: 1
  • 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: 3
  • 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: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss Validation Loss pittsburgh_dot_map@100
0 0 - - 0.5984
0.8 100 0.587 0.1954 0.7780
1.592 200 0.1828 0.1805 0.8020
2.384 300 0.2224 0.1605 0.8263

Framework Versions

  • Python: 3.12.7
  • Sentence Transformers: 3.2.0
  • Transformers: 4.45.2
  • PyTorch: 2.2.2+cu121
  • Accelerate: 1.0.1
  • Datasets: 3.0.1
  • Tokenizers: 0.20.1

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",
}

MultipleNegativesRankingLoss

@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}
}