metadata

base_model: BAAI/bge-base-en-v1.5
datasets: []
language: []
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:76
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: ply C Tris  pH8.0 Dextran Trehalose dNTPS Na2 SO4 Triton X-100
    sentences:
      - NE
      - Assignee
      - ID  NO
  - source_sentence: >-
      Certain  isothermal  amplification  methods  are  able  to amplify a
      target nucleic  acid from  trace levels to  very high  25 and  detectable 
      levels  within  a  matter  of  minutes.  Such isothermal  methods,  e.g., 
      Nicking  and  Extension Amplifi cation Reaction  (NEAR),  allow users  to 
      detect  a  particular nucleotide  sequence in trace  amounts, 
      facilitating point-of care  testing  and  increasing  the  accessibility 
      and  speed  of diagnostics. Streptococcus pyogenes is  the causative agent
      of group A streptococcal  (GAS)  infections  such  as  pharyngitis,  impe
      tigo,  and  life-threatening  necrotizing  fasciitis  and  sepsis. The 
      most  common  GAS  infection,  pharyngitis,  can  be diagnosed by
      collecting a throat swab sample from a patient and culturing the sample
      under conditions that would enable bacterial,  specifically S.  pyogenes, 
      growth, which takes 2-3 days.  Culturing  S.  pyogenes  is  an  accurate 
      and  reliable method of diagnosing GAS,  but it  is  slow. A 2-3  day
      delay in prescribing appropriate  antibiotic treatment can result in
      unnecessary patient suffering and potentially the onset oflife threatening
      conditions such as rheumatic fever.  In the recent past, biochemical
      methods have been developed to detect S. pyogenes,  but these  methods 
      do  not  provide  the  necessary characteristics  to  be  deployed  in 
      the  point-of-care  setting, either due  to  a  lack of sensitivity  or
      time to  result  (speed). Accordingly, a highly sensitive and rapid
      qualitative assay for the detection and diagnosis of a S.  pyogenes
      infection is desired.
    sentences:
      - ABSTRACT
      - TACTGTTCCTGTTTGA
      - BACKGROUND
  - source_sentence: W02010/141940 12/2010 CA (US)
    sentences:
      - WO
      - DESCRIPTION OF  DRAWINGS
      - SUMMARY
  - source_sentence: 9 6 nucleotide sequence at least 80,  85  or 95%  identity to  SEQ
    sentences:
      - '2'
      - NEAR
      - Ph.  Dissertation published Jan.  1,  2008.  (Year
  - source_sentence: >-
      One potential inhibitor of the NEAR technology is human gDNA.  When  a 
      throat  swab  sample  is  collected  from  a patient  symptomatic  for 
      GAS  infection,  it  is  possible  that human gDNA is  also  collected on
      the  swab  (from immune cells such as white blood cells or from local
      epithelial cells). In order to  assess the  impact that human gDNA has on
      the GAS  assay,  a  study  was  performed  using  three  different levels 
      of GAS  gDNA  (25,  250  and  1000  copies)  in  the presence of 0,  10, 
      50,  100,  250,  500  or  1000 ng  of human gDNA. As  shown in FIG.  6, 
      the presence of human gDNA does  have  an  impact  on GAS  assay 
      performance,  and  the impact is  GAS  target concentration dependent. 
      When there is  a low copy number of target GAS present in the reaction,
      10  ng  of human  gDNA  or  more  significantly  inhibits  the assay. At
      250 copies  of GAS target,  the impact of 10 ng of 60  human gDNA is 
      less, and at 1,000 copies of GAS target, the effect of 10 ng of human gDNA
      on the assay is  significantly less.  In fact,  when  1,000  copies  of
      target  is  present  in the assay, up to  100 ng of human gDNA can be
      tolerated, albeit with a slower amplification speed and reduced
      fluorescence signal.  Testing  of the  501  (IC  only)  mix  showed  a 
      more robust  response  to  human  gDNA.  When  the  501  mix  was tested
      in the presence of O copies  of target  GAS  and up  to US  10,329,601  B2
      23 1,000 ng of human gDNA, the assay still produced a clearly positive
      signal at 500 ng of human gDNA (even at 1,000 ng of human  gDNA  the 
      fluorescence  signal  was  still  above background). Other Embodiments It 
      is  to  be understood  that  while  the  invention has  been described 
      in  conjunction  with  the  detailed  description 24 thereof,  the 
      foregoing  description  is  intended  to  illustrate and not limit the
      scope of the invention, which is  defined by the scope of the appended
      claims. Other aspects, advantages, and  modifications  are  within  the 
      scope  of the  following claims.
    sentences:
      - '75'
      - TGTAGCTGACACCACCAAGCTACA
      - Impact  of Human Genomic  DNA (gDNA)  on GAS Assay
model-index:
  - name: SentenceTransformer based on BAAI/bge-base-en-v1.5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.625
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.875
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.625
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.29166666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.625
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.875
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8202007889556063
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7604166666666666
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7604166666666666
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.625
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.875
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.875
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.625
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.29166666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17500000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.625
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.875
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.875
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.810892117584935
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.75
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.75
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.625
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.875
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.875
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.625
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.29166666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17500000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.625
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.875
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.875
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8057993287946483
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7447916666666666
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7447916666666666
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.75
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.875
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.75
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.29166666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.75
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.875
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8608566009043177
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8166666666666667
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8166666666666667
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.75
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.875
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.75
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.29166666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.2
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.75
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.875
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8608566009043177
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8166666666666667
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8166666666666667
            name: Cosine Map@100

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

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("kr-manish/fine-tuned-bge-base-raw_pdf-v1")
# Run inference
sentences = [
    'One potential inhibitor of the NEAR technology is human gDNA.  When  a  throat  swab  sample  is  collected  from  a patient  symptomatic  for  GAS  infection,  it  is  possible  that human gDNA is  also  collected on the  swab  (from immune cells such as white blood cells or from local epithelial cells). In order to  assess the  impact that human gDNA has on the GAS  assay,  a  study  was  performed  using  three  different levels  of GAS  gDNA  (25,  250  and  1000  copies)  in  the presence of 0,  10,  50,  100,  250,  500  or  1000 ng  of human gDNA. As  shown in FIG.  6,  the presence of human gDNA does  have  an  impact  on GAS  assay  performance,  and  the impact is  GAS  target concentration dependent.  When there is  a low copy number of target GAS present in the reaction, 10  ng  of human  gDNA  or  more  significantly  inhibits  the assay. At 250 copies  of GAS target,  the impact of 10 ng of 60  human gDNA is  less, and at 1,000 copies of GAS target, the effect of 10 ng of human gDNA on the assay is  significantly less.  In fact,  when  1,000  copies  of target  is  present  in the assay, up to  100 ng of human gDNA can be tolerated, albeit with a slower amplification speed and reduced fluorescence signal.  Testing  of the  501  (IC  only)  mix  showed  a  more robust  response  to  human  gDNA.  When  the  501  mix  was tested in the presence of O copies  of target  GAS  and up  to US  10,329,601  B2 23 1,000 ng of human gDNA, the assay still produced a clearly positive signal at 500 ng of human gDNA (even at 1,000 ng of human  gDNA  the  fluorescence  signal  was  still  above background). Other Embodiments It  is  to  be understood  that  while  the  invention has  been described  in  conjunction  with  the  detailed  description 24 thereof,  the  foregoing  description  is  intended  to  illustrate and not limit the scope of the invention, which is  defined by the scope of the appended claims. Other aspects, advantages, and  modifications  are  within  the  scope  of the  following claims.',
    'Impact  of Human Genomic  DNA (gDNA)  on GAS Assay',
    'TGTAGCTGACACCACCAAGCTACA',
]
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

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.625
cosine_accuracy@3	0.875
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.625
cosine_precision@3	0.2917
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.625
cosine_recall@3	0.875
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8202
cosine_mrr@10	0.7604
cosine_map@100	0.7604

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.625
cosine_accuracy@3	0.875
cosine_accuracy@5	0.875
cosine_accuracy@10	1.0
cosine_precision@1	0.625
cosine_precision@3	0.2917
cosine_precision@5	0.175
cosine_precision@10	0.1
cosine_recall@1	0.625
cosine_recall@3	0.875
cosine_recall@5	0.875
cosine_recall@10	1.0
cosine_ndcg@10	0.8109
cosine_mrr@10	0.75
cosine_map@100	0.75

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.625
cosine_accuracy@3	0.875
cosine_accuracy@5	0.875
cosine_accuracy@10	1.0
cosine_precision@1	0.625
cosine_precision@3	0.2917
cosine_precision@5	0.175
cosine_precision@10	0.1
cosine_recall@1	0.625
cosine_recall@3	0.875
cosine_recall@5	0.875
cosine_recall@10	1.0
cosine_ndcg@10	0.8058
cosine_mrr@10	0.7448
cosine_map@100	0.7448

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.75
cosine_accuracy@3	0.875
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.75
cosine_precision@3	0.2917
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.75
cosine_recall@3	0.875
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8609
cosine_mrr@10	0.8167
cosine_map@100	0.8167

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.75
cosine_accuracy@3	0.875
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.75
cosine_precision@3	0.2917
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.75
cosine_recall@3	0.875
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8609
cosine_mrr@10	0.8167
cosine_map@100	0.8167

Training Details

Training Dataset

Unnamed Dataset

Size: 76 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 8 tokens
mean: 148.89 tokens
max: 512 tokens

min: 3 tokens
mean: 6.54 tokens
max: 18 tokens

	positive	anchor
type	string	string
details	min: 8 tokens mean: 148.89 tokens max: 512 tokens	min: 3 tokens mean: 6.54 tokens max: 18 tokens

Samples:

positive	anchor
`1 (AGACTCCATATGGAGTCTAGC CAAACAGGAACA); a reverse template comprising a nucleotide sequence having at least 80, 85 or 95% identity 55 (CGACTCCATATGGAGTC to GAAAGCAATCTGAGGA); and a probe oligonucleotide comprising a nucleotide sequence at least 80, 85 or 95%`	`to SEQ ID NO`
One potential inhibitor of the NEAR technology is human gDNA. When a throat swab sample is collected from a patient symptomatic for GAS infection, it is possible that human gDNA is also collected on the swab (from immune cells such as white blood cells or from local epithelial cells). In order to assess the impact that human gDNA has on the GAS assay, a study was performed using three different levels of GAS gDNA (25, 250 and 1000 copies) in the presence of 0, 10, 50, 100, 250, 500 or 1000 ng of human gDNA. As shown in FIG. 6, the presence of human gDNA does have an impact on GAS assay performance, and the impact is GAS target concentration dependent. When there is a low copy number of target GAS present in the reaction, 10 ng of human gDNA or more significantly inhibits the assay. At 250 copies of GAS target, the impact of 10 ng of 60 human gDNA is less, and at 1,000 copies of GAS target, the effect of 10 ng of human gDNA on the assay is significantly less. In fact, when 1,000 copies of target is present in the assay, up to 100 ng of human gDNA can be tolerated, albeit with a slower amplification speed and reduced fluorescence signal. Testing of the 501 (IC only) mix showed a more robust response to human gDNA. When the 501 mix was tested in the presence of O copies of target GAS and up to US 10,329,601 B2 23 1,000 ng of human gDNA, the assay still produced a clearly positive signal at 500 ng of human gDNA (even at 1,000 ng of human gDNA the fluorescence signal was still above background). Other Embodiments It is to be understood that while the invention has been described in conjunction with the detailed description 24 thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.	`Impact of Human Genomic DNA (gDNA) on GAS Assay`
25C-4x lyophilization mix, single tube assay format; 50C-2x lyophilization mix, single tube assay format; 25T-4x lyophilization mix, target assay only; 50T-2x lyophilization mix, target assay only; 25I- 4x lyophilization mix, IC assay only; 50I-2x lyophilization mix, internal control (IC) assay only. The GAS NEAR assay can be run on an appropriate platform. For example, the GAS NEAR assay can be run on an Al ere i platform (www.alere.com/ww/en/product-details/ alere-i-strep-a.html). AnA!ere i system consists of an instru ment which provides heating, mixing and fluorescence detection with automated result output, and a set of dispos ables, consisting of the sample receiver (where the elution buffer is stored), a test base ( containing two tubes of lyophilized NEAR reagents) and a transfer device ( designed to transfer 100 µI aliquots of eluted sample from the sample receiver to each of the two tubes containing lyophilized NEAR reagents located in the test base). Suitable dispos ables for use with the Alere i GAS NEAR test include those 60 described in, for example U.S. application Ser. No. 13/242, 999, incorporated herein by reference in its entirety. In addition to containing the reagents necessary for driv ing the GAS NEAR assay, the lyophilized material also contains the lytic agent for GAS, the protein plyC; therefore, 65 GAS lysis does not occur until the lyophilized material is re-suspended. In some cases, the lyophilized material does not contain a lytic agent for GAS, for example, in some US 10,329,601 B2 19 examples, the lyophilized material does not contain the protein plyC. The elution buffer was designed to allow for the rapid release of GAS organisms from clinical sample throat swabs as well as to provide the necessary salts for driving the NEAR assay (both MgSO4 and (NH4)2SO4), in 5 a slightly basic environment. In some examples, the elution buffer also includes an anti-microbial agent or preservative (e.g., ProClin® 950). For the present examples, GAS assay was performed as a two tube assay-a GAS target specific assay in one tube, and an internal control (IC) assay in a second tube (tested side by side on the Alere i).	`annotated as follows`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 3e-05
num_train_epochs: 40
lr_scheduler_type: cosine
warmup_ratio: 0.2
fp16: True
load_best_model_at_end: True
optim: adamw_torch_fused

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
learning_rate: 3e-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: 40
max_steps: -1
lr_scheduler_type: cosine
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: 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}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0	0	-	0.2103	0.1702	0.1888	0.1783	0.1815
1.0	1	-	0.2102	0.1702	0.1888	0.1783	0.1815
2.0	2	-	0.2104	0.1705	0.1890	0.1797	0.1815
3.0	3	-	0.2841	0.1733	0.2524	0.1997	0.2465
4.0	5	-	0.3285	0.2747	0.2865	0.3281	0.2901
5.0	6	-	0.3311	0.3045	0.2996	0.3930	0.3001
6.0	7	-	0.3948	0.3808	0.3193	0.4576	0.3147
7.0	9	-	0.5308	0.4366	0.4222	0.5445	0.4367
8.0	10	3.233	0.5352	0.5240	0.5224	0.5867	0.4591
9.0	11	-	0.5438	0.5864	0.5228	0.6519	0.6475
10.0	13	-	0.6540	0.5906	0.6554	0.6684	0.6511
11.0	14	-	0.6585	0.6020	0.6684	0.6857	0.6621
12.0	15	-	0.6632	0.6661	0.6798	0.7063	0.6685
13.0	17	-	0.7292	0.7210	0.6971	0.7396	0.7062
14.0	18	-	0.7396	0.7375	0.7229	0.8333	0.7068
15.0	19	-	0.75	0.7438	0.7021	0.8333	0.7083
16.0	20	1.4113	0.7604	0.7292	0.7042	0.8229	0.7104
17.0	21	-	0.7542	0.7262	0.7095	0.8229	0.7158
18.0	22	-	0.7438	0.7344	0.7054	0.8167	0.7188
19.0	23	-	0.8063	0.7344	0.7125	0.8125	0.7021
20.0	25	-	0.7958	0.7344	0.7262	0.8125	0.7333
21.0	26	-	0.8021	0.7344	0.7470	0.8095	0.7333
22.0	27	-	0.8021	0.7344	0.7470	0.8095	0.7333
23.0	29	-	0.8021	0.7344	0.7470	0.8095	0.7438
24.0	30	0.6643	0.8021	0.7448	0.7470	0.8125	0.7438
25.0	31	-	0.8125	0.7448	0.7470	0.8125	0.7604
26.0	33	-	0.8125	0.7448	0.75	0.8167	0.7604
27.0	34	-	0.8125	0.7448	0.75	0.8167	0.7604
28.0	35	-	0.8125	0.7448	0.75	0.8167	0.7604
29.0	37	-	0.8167	0.7448	0.75	0.8167	0.7604
30.0	38	-	0.8167	0.7448	0.75	0.8167	0.7604
31.0	39	-	0.8167	0.7448	0.75	0.8167	0.7604
32.0	40	0.4648	0.8167	0.7448	0.75	0.8167	0.7604

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}