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Add new SentenceTransformer model

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:111470
  - loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-small
widget:
  - source_sentence: when was the first elephant brought to america
    sentences:
      - >-
        Old Bet The first elephant brought to the United States was in 1796,
        aboard the America which set sail from Calcutta for New York on December
        3, 1795.[4] However, it is not certain that this was Old Bet.[2] The
        first references to Old Bet start in 1804 in Boston as part of a
        menagerie.[1] In 1808, while residing in Somers, New York, Hachaliah
        Bailey purchased the menagerie elephant for $1,000 and named it "Old
        Bet".[5][6]
      - >-
        Cronus Rhea secretly gave birth to Zeus in Crete, and handed Cronus a
        stone wrapped in swaddling clothes, also known as the Omphalos Stone,
        which he promptly swallowed, thinking that it was his son.
      - >-
        Renal artery One or two accessory renal arteries are frequently found,
        especially on the left side since they usually arise from the aorta, and
        may come off above (more common) or below the main artery. Instead of
        entering the kidney at the hilus, they usually pierce the upper or lower
        part of the organ.
  - source_sentence: who won the india's next superstar grand finale
    sentences:
      - >-
        India's Next Superstars India's Next Superstars is a talent-search
        Indian reality TV show, which premiered on Star Plus and is streamed on
        Hotstar.[1] Karan Johar and Rohit Shetty are the judges for the show.
        [2] Aman Gandotra and Natasha Bharadwaj were declared winners of 2018
        season. Shruti Sharma won a 'Special Mention' award. Runners up in the
        male category were Aashish Mehrotra and Harshvardhan Deo and in the
        female category were Naina Singh and Shruti Sharma. [3]
      - >-
        India national cricket team India was invited to The Imperial Cricket
        Council in 1926, and made their debut as a Test playing nation in
        England in 1932, led by CK Nayudu, who was considered as the best Indian
        batsman at the time.[14] The one-off Test match between the two sides
        was played at Lord's in London. The team was not strong in their batting
        at this point and went on to lose by 158 runs.[15] In 1933, the first
        Test series in India was played between India and England with matches
        in Bombay, Calcutta (now Kolkata) and Madras (now Chennai). England won
        the series 2–0.[16] The Indian team continued to improve throughout the
        1930s and '40s but did not achieve an international victory during this
        period. In the early 1940s, India didn't play any Test cricket due to
        the Second World War. The team's first series as an independent country
        was in late 1947 against Sir Donald Bradman's Invincibles (a name given
        to the Australia national cricket team of that time). It was also the
        first Test series India played which was not against England. Australia
        won the five-match series 4–0, with Bradman tormenting the Indian
        bowling in his final Australian summer.[17] India subsequently played
        their first Test series at home not against England against the West
        Indies in 1948. West Indies won the 5-Test series 1–0.[18]
      - >-
        Hindi Medium (film) Raj Batra (Irrfan Khan) is a rich businessman from
        Delhi staying with his wife Mita (Saba Qamar). They studied in a Hindi
        Medium school but wants their 5 year old daughter, Pia (Dishita Sehgal),
        to be admitted to one of the top schools in Delhi. The top school,
        'Delhi Grammar School', has a condition that they will admit students
        who reside within 3km radius, so the family moves to Vasant Vihar.
  - source_sentence: i am human and nothing of that which is human is alien to me meaning
    sentences:
      - >-
        America's Got Talent Introduced in season nine, the "Golden Buzzer" is
        located on the center of the judges' desk and may be used once per
        season by each judge. In season 9, a judge could press the golden buzzer
        to save an act from elimination, regardless of the number of X's earned
        from the other judges. Starting in season 10 and onward, any act that
        receives a golden buzzer advances directly to the live show; and in
        season 11, the hosts also were given the power to use the golden buzzer.
        The golden buzzer is also used in the Judge Cuts format.
      - >-
        You'll Never Walk Alone "You'll Never Walk Alone" is a show tune from
        the 1945 Rodgers and Hammerstein musical Carousel. In the second act of
        the musical, Nettie Fowler, the cousin of the female protagonist Julie
        Jordan, sings "You'll Never Walk Alone" to comfort and encourage Julie
        when her husband, Billy Bigelow, the male lead, commits suicide after a
        failed robbery attempt. It is reprised in the final scene to encourage a
        graduation class of which Louise (Billy and Julie's daughter) is a
        member. The now invisible Billy, who has been granted the chance to
        return to Earth for one day in order to redeem himself, watches the
        ceremony and is able to silently motivate the unhappy Louise to join in
        the song.
      - >-
        Terence One famous quotation by Terence reads: "Homo sum, humani nihil a
        me alienum puto", or "I am human, and I think that nothing of that which
        is human is alien to me." This appeared in his play Heauton Timorumenos.
  - source_sentence: what do glial cells do in the brain
    sentences:
      - >-
        Neuroglia Neuroglia, also called glial cells or simply glia, are
        non-neuronal cells in the central nervous system (brain and spinal cord)
        and the peripheral nervous system. They maintain homeostasis, form
        myelin, and provide support and protection for neurons.[1] In the
        central nervous system, glial cells include oligodendrocytes,
        astrocytes, ependymal cells and microglia, and in the peripheral nervous
        system glial cells include Schwann cells and satellite cells. They have
        four main functions: (1) To surround neurons and hold them in place (2)
        To supply nutrients and oxygen to neurons (3) To insulate one neuron
        from another (4) To destroy pathogens and remove dead neurons. They also
        play a role in neurotransmission and synaptic connections,[2] and in
        physiological processes like breathing,[3][4] .
      - >-
        The Mother (How I Met Your Mother) Tracy McConnell, better known as "The
        Mother", is the title character from the CBS television sitcom How I Met
        Your Mother. The show, narrated by Future Ted, tells the story of how
        Ted Mosby met The Mother. Tracy McConnell appears in 8 episodes from
        "Lucky Penny" to "The Time Travelers" as an unseen character; she was
        first seen fully in "Something New" and was promoted to a main character
        in season 9. The Mother is played by Cristin Milioti.
      - >-
        Marsupial Marsupials are any members of the mammalian infraclass
        Marsupialia. All extant marsupials are endemic to Australasia and the
        Americas. A distinctive characteristic common to these species is that
        most of the young are carried in a pouch. Well-known marsupials include
        kangaroos, wallabies, koalas, possums, opossums, wombats, and Tasmanian
        devils. Some lesser-known marsupials are the potoroo and the quokka.
  - source_sentence: 'It was Easipower that said :'
    sentences:
      - >-
        United States presidential election However, federal law does specify
        that all electors must be selected on the same day, which is "the first
        Tuesday after the first Monday in November," i.e., a Tuesday no earlier
        than November 2 and no later than November 8.[17] Today, the states and
        the District of Columbia each conduct their own popular elections on
        Election Day to help determine their respective slate of electors. Thus,
        the presidential election is really an amalgamation of separate and
        simultaneous state elections instead of a single national election run
        by the federal government.
      - It is said that Easipower was ,
      - 'It was Easipower that said :'
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 thenlper/gte-small
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.34
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.56
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.64
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.76
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.34
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18666666666666668
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.128
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07600000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.34
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.56
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.64
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.76
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5416219337167224
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.47319047619047616
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4857841065799604
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.54
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.76
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.54
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.24
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.15600000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.086
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.52
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.66
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.71
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.77
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6525146735767775
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6275
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6140321846592789
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.44000000000000006
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.63
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.78
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.44000000000000006
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.21333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14200000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.081
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.43000000000000005
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6100000000000001
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.675
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.765
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5970683036467499
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.550345238095238
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5499081456196196
            name: Cosine Map@100

SentenceTransformer based on thenlper/gte-small

This is a sentence-transformers model finetuned from thenlper/gte-small. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: thenlper/gte-small
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity

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': 128, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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("redis/model-b-structured")
# Run inference
sentences = [
    'It was Easipower that said :',
    'It was Easipower that said :',
    'It is said that Easipower was ,',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.8522],
#         [1.0000, 1.0000, 0.8522],
#         [0.8522, 0.8522, 1.0000]])

Evaluation

Metrics

Information Retrieval

Datasets: NanoMSMARCO and NanoNQ
Evaluated with InformationRetrievalEvaluator

Metric	NanoMSMARCO	NanoNQ
cosine_accuracy@1	0.34	0.54
cosine_accuracy@3	0.56	0.7
cosine_accuracy@5	0.64	0.76
cosine_accuracy@10	0.76	0.8
cosine_precision@1	0.34	0.54
cosine_precision@3	0.1867	0.24
cosine_precision@5	0.128	0.156
cosine_precision@10	0.076	0.086
cosine_recall@1	0.34	0.52
cosine_recall@3	0.56	0.66
cosine_recall@5	0.64	0.71
cosine_recall@10	0.76	0.77
cosine_ndcg@10	0.5416	0.6525
cosine_mrr@10	0.4732	0.6275
cosine_map@100	0.4858	0.614

Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with NanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nq"
    ],
    "dataset_id": "lightonai/NanoBEIR-en"
}

Metric	Value
cosine_accuracy@1	0.44
cosine_accuracy@3	0.63
cosine_accuracy@5	0.7
cosine_accuracy@10	0.78
cosine_precision@1	0.44
cosine_precision@3	0.2133
cosine_precision@5	0.142
cosine_precision@10	0.081
cosine_recall@1	0.43
cosine_recall@3	0.61
cosine_recall@5	0.675
cosine_recall@10	0.765
cosine_ndcg@10	0.5971
cosine_mrr@10	0.5503
cosine_map@100	0.5499

Training Details

Training Dataset

Unnamed Dataset

Size: 111,470 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 13.22 tokens max: 44 tokens	min: 6 tokens mean: 90.67 tokens max: 128 tokens	min: 7 tokens mean: 89.65 tokens max: 128 tokens

Samples:

anchor	positive	negative
`which state is home to the arizona ice tea beverage company`	`Arizona Beverage Company Arizona Beverages USA (stylized as AriZona) is an American producer of many flavors of iced tea, juice cocktails and energy drinks based in Woodbury, New York.[2] Arizona's first product was made available in 1992.`	`Arya Vaishya Arya Vaishya (Arya Vysya) is an Indian caste. Orthodox Arya Vaishyas follow rituals prescribed in the Vasavi Puranam, a religious text written in the late Middle Ages. Their kuladevata is Vasavi. The community were formerly known as Komati Chettiars in Tamil Nadu but now prefer to be referred to as Arya Vaishya.[1]`
`when were afro-american and africana studies programs founded in colleges and universities`	African-American studies Programs and departments of African-American studies were first created in the 1960s and 1970s as a result of inter-ethnic student and faculty activism at many universities, sparked by a five-month strike for black studies at San Francisco State. In February 1968, San Francisco State hired sociologist Nathan Hare to coordinate the first black studies program and write a proposal for the first Department of Black Studies; the department was created in September 1968 and gained official status at the end of the five-months strike in the spring of 1969. The creation of programs and departments in Black studies was a common demand of protests and sit-ins by minority students and their allies, who felt that their cultures and interests were underserved by the traditional academic structures.	`Maze Runner: The Death Cure Maze Runner: The Death Cure was originally set to be released on February 17, 2017, in the United States by 20th Century Fox, but the studio rescheduled the film's release for January 26, 2018 in theatres and IMAX, allowing time for O'Brien to recover from injuries he sustained during filming. The film received mixed reviews from critics and grossed over $284Â million worldwide.`
`who recorded the song total eclipse of the heart`	`Bonnie Tyler Bonnie Tyler (born Gaynor Hopkins; 8 June 1951) is a Welsh singer, known for her distinctive husky voice. Tyler came to prominence with the release of her 1977 album The World Starts Tonight and its singles "Lost in France" and "More Than a Lover". Her 1978 single "It's a Heartache" reached number four on the UK Singles Chart, and number three on the US Billboard Hot 100.`	`Manny Pacquiao vs. Juan Manuel Márquez IV Marquez defeated Pacquiao by knockout with one second remaining in the sixth round. It was named Fight of the Year and Knockout of the Year by Ring Magazine, with round five garnering Round of the Year honors.[2]`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 12,386 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 13.03 tokens max: 44 tokens	min: 6 tokens mean: 89.36 tokens max: 128 tokens	min: 4 tokens mean: 88.87 tokens max: 128 tokens

Samples:

anchor	positive	negative
`In early July , Steve Whitley , the criminal father of Harper Whitley and Garrett Whitley , and brother of Benny Cameron .`	`In early July , Steve Whitley , the criminal father of Harper Whitley and Garrett Whitley , and brother of Benny Cameron .`	`In early July , Garrett Whitley , who is the criminal father of Harper Whitley and Steve Whitley , and the brother of Benny Cameron , appeared .`
`when will the next season of house of cards be released`	`House of Cards (season 6) The sixth and final season of the American political drama web television series House of Cards was confirmed by Netflix on December 4, 2017, and is scheduled to be released on November 2, 2018. Unlike previous seasons that consisted of thirteen episodes each, the sixth season will consist of only eight. The season will not include former lead actor Kevin Spacey, who was fired from the show due to sexual misconduct allegations.`	Wild 'n Out For the first four seasons, the show filmed from Los Angeles/Hollywood and aired on MTV. The first run episodes were suspended as Mr. Renaissance Entertainment became Ncredible Entertainment in 2012. Upon being revived in 2012, the show was produced in New York City and aired on MTV2 during Seasons 5â€“7, it also returned to that location for Season 9. In 2016, the show returned to airing new episodes on MTV and also for the first time since Season 4, production is in Los Angeles.
`who played the father on father knows best`	Father Knows Best The series began August 25, 1949, on NBC Radio. Set in the Midwest, it starred Robert Young as the General Insurance agent Jim Anderson. His wife Margaret was first portrayed by June Whitley and later by Jean Vander Pyl. The Anderson children were Betty (Rhoda Williams), Bud (Ted Donaldson), and Kathy (Norma Jean Nilsson). Others in the cast were Eleanor Audley, Herb Vigran and Sam Edwards. Sponsored through most of its run by General Foods, the series was heard Thursday evenings on NBC until March 25, 1954.	List of To Kill a Mockingbird characters Maycomb children believe he is a horrible person, due to the rumors spread about him and a trial he underwent as a teenager. It is implied during the story that Boo is a very lonely man who attempts to reach out to Jem and Scout for love and friendship, such as leaving them small gifts and figures in a tree knothole. Scout finally meets him at the very end of the book, when he saves the children's lives from Bob Ewell. Scout describes him as being sickly white, with a thin mouth, thin and feathery hair, and grey eyes, almost as if he were blind. During the same night, when Boo whispers to Scout to walk him back to the Radley house, Scout takes a moment to picture what it would be like to be Boo Radley. While standing on his porch, she realizes his "exile" inside his house is really not that lonely.

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 8e-05
weight_decay: 0.005
max_steps: 1125
warmup_ratio: 0.1
fp16: True
dataloader_drop_last: True
dataloader_num_workers: 1
dataloader_prefetch_factor: 1
load_best_model_at_end: True
optim: adamw_torch
ddp_find_unused_parameters: False
push_to_hub: True
hub_model_id: redis/model-b-structured
eval_on_start: True

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: 128
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: 8e-05
weight_decay: 0.005
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3.0
max_steps: 1125
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
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: True
dataloader_num_workers: 1
dataloader_prefetch_factor: 1
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: False
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: True
resume_from_checkpoint: None
hub_model_id: redis/model-b-structured
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
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
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: True
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0	0	-	1.9462	0.6259	0.6583	0.6421
0.2874	250	0.3773	0.0669	0.5322	0.6570	0.5946
0.5747	500	0.0787	0.0564	0.5584	0.6307	0.5946
0.8621	750	0.0678	0.0495	0.5390	0.6447	0.5918
1.1494	1000	0.0517	0.0479	0.5416	0.6525	0.5971

Framework Versions

Python: 3.10.18
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 2.21.0
Tokenizers: 0.22.1

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}
}

SentenceTransformer based on thenlper/gte-small

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Nano BEIR

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

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