metadata

base_model: mixedbread-ai/deepset-mxbai-embed-de-large-v1
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:1814
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-

      The document you provided seems to be a list of compounds, some of which
      are well-known drugs or drugs used in experimental contexts, and others
      that don't appear to have recognized applications in medicine or science.
      The list includes substances like cortisol, a hormone, and filopram, which
      is related to anesthetics or possibly a misprint or misclassification. The
      side effects listed are also a mix, with some being plausible reactions to
      certain medication (like Edema, dysphagia) and others being quite unusual
      for commonly recognized drug interactions (like retinal vein occlusion,
      which is not a typical side effect of most medications).


      It would be useful to have labels or references indicating which of these
      compounds are actually drugs and which are other chemical substances. For
      instance, cortisol, if given its correct context, would typically have
      side effects associated with cortisol therapy such as fluid retention or
      electrolyte imbalances.


      If you need detailed information on how these substances work or what
      their possible side effects might be, you'll likely need to refer to a
      medical compendium or a pharmacology resource for accurate data. It's also
      important to clarify the intended use for this list, whether for
      educational purposes, research, or another context; the provided list
      appears to be a jumbled amalgamation, which might not have clear clinical
      relevance without additional detail.
    sentences:
      - >-
        Can you suggest medications targeting the GC gene/protein with a proven
        synergy with AVE9633?
      - >-
        Could you help identify the gene or protein that facilitates
        sodium-dependent transportation and elimination of organic anions, with
        a particular emphasis on those implicated in the cellular efflux of
        potentially hazardous organic anions? Additionally, I'm interested in
        understanding if this gene or protein also mediates the transport of
        drugs known to exhibit synergistic pharmacological interactions with
        Ractopamine.
      - >-
        Can you list the medications suitable for benign prostatic hyperplasia
        and tell me if any are linked to dysphagia as a side effect?
  - source_sentence: >-


      The provided information describes a gene that plays a role in multiple
      biological processes and is linked with certain diseases. Here
    sentences:
      - >-
        Which genes or proteins interact with the "Regulation of HSF1-mediated
        heat shock response" pathway and also engage in protein-protein
        interactions with PRNP?
      - >-
        Which anatomical parts lack the expression of genes or proteins involved
        in the L-alanine degradation pathway?
      - >-
        What is the name of a disease classified as a variant or subtype of
        sinoatrial node disease in the latest medical disease taxonomy?
  - source_sentence: >-

      The list you've provided contains a variety of medications, including
      antidepressants, antihistamines, anxiolytics, and more. Here's a breakdown
      by category:


      ### Antidepressants

      - **Amphetamine**

      - **Cevimeline**

      - **Esmolol**

      - **Bortezomib**

      - **
    sentences:
      - >-
        What are some related conditions to triple-negative breast cancer that
        could be causing persistent fatigue?
      - >-
        Which medication is effective against simple Plasmodium falciparum
        infections and functions by engaging with genes or proteins that
        interact with the minor groove of DNA rich in adenine and thymine?
      - >-
        Which diseases associated with SRSF2 gene mutations are primarily found
        in adults and the elderly?
  - source_sentence: >-


      The drug mentioned in the query is "Dabigatran". It belongs to the class
      of drugs known as direct thrombin inhibitors. This class of drugs is used
      primarily for the prevention and treatment of thromboembolic disorders.


      Regarding potential side effects, they include:

      1. Inflammatory abnormality of the skin (Erythema)

      2. Hemolytic anemia (a type of anemia where red blood cells are destroyed
      prematurely)

      3. Thrombocytopenia (low platelet count)

      4. Pancytopenia (a decrease in the number of all types of blood cells -
      red, white, and platelet cells)

      5. Fever

      6. Pain

      7. Seizure

      8. Headache

      9. Vomiting

      10. Abdominal pain

      11. Hyperactivity

      12. Erythroderma (a type of skin flare characterized by a redness over the
      trunk and limbs)

      13. Vertigo (a sensation of spinning or motion)

      14. Granulocytopenia (low neutrophil count)

      15. Pruritus (severe itching)

      16. Confusion

      17. Eosinophilia (a condition characterized by an increased number of
      eosinophils, a type of white blood cell)

      18. Anaphylactic shock (a serious allergic reaction)

      19. Hyperkinetic movements

      20. Nausea

      21. Acute sinusitis (inflammation of the sinus cavities)

      22. Agitation

      23. Excessive daytime somnolence (excessively sleepy during the day)

      24. Aplastic anemia (a condition where the bone marrow fails to produce
      enough new blood cells)

      25. Increased blood urea nitrogen (BUN) (a marker of kidney function,
      indicating the kidneys are not working properly)

      26. Prolonged prothrombin time (an indication of an increased risk of
      bleeding, due to a reduction in clotting protein)

      27. Recurrent tonsillitis (repeated inflammation of the tonsils)


      Dabigatran works by inhibiting thrombin (Factor IIa), an enzyme involved
      in the clotting process. If any of these side effects are experienced, it
      is important to seek medical attention or consult with a healthcare
      provider.
    sentences:
      - >-
        What are the clinical manifestations or phenotypic characteristics
        associated with the subtype of myocardial infarction known as
        posteroinferior?
      - >-
        Could you supply a list of drugs prescribed for respiratory infections
        that may also lead to side effects like hemolytic anemia and nausea?
      - >-
        Which diseases are associated with the FAM111A gene that present with
        both skeletal and endocrine system abnormalities?
  - source_sentence: >-

      The list you provided seems to be a mix of various chemical substances,
      some of which appear to be medications, others are chemical compounds, and
      a few could be substances from other fields (e.g., water treatment, food
      additives). To be more precise, it would be helpful to categorize them
      properly based on their common usage:


      ### Medications and Drugs:

      - **Antibiotics**: Cefoxitin, Tobramycin, Amikacin

      - ** pain and inflammation relievers**: Benoxaprofen, Daptomycin,
      Ceftolozane, Salicylates (Benzydamine, Dexamethasone sodium phosphate)

      - **Intravenous fluids**: Magnesium trisilicate

      - **Antivirals**: Ribavirin, Inotersen

      - **Antibacterial agents**: Epirizole, Floctafenine, Flunixin

      - **Vaccines**: Vaborbactam, Brincidofovir, Adefovir

      - **Neuromodulators**: Cefatrizine, Bumadizone, Alminoprofen

      - **Cancer treatments**: Colistin, Nitrofurantoin, Sisomicin


      ### Chemical Compounds:

      - **Salts and salts of acidity**: Fosfomycin, Azosemide, Mofebutazone

      - **Amino acids**: Phenylalanine, Nitrosalicylic
    sentences:
      - >-
        Is there a regulatory function associated with the epidermal growth
        factor receptor or its interacting proteins in the control of genes or
        proteins that participate in the inactivation of fast sodium channels
        during Phase 1 of cardiac action potential propagation?
      - >-
        Which diseases, either as subtypes or complications, should be
        considered when a patient shows symptoms suggesting neoplastic
        syndromes?
      - Which drugs interact with the SERPINA1 gene/protein as carriers?
model-index:
  - name: SentenceTransformer based on mixedbread-ai/deepset-mxbai-embed-de-large-v1
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.3910891089108911
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4752475247524752
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.49504950495049505
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5544554455445545
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.3910891089108911
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15841584158415842
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09900990099009901
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05544554455445544
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3910891089108911
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4752475247524752
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.49504950495049505
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5544554455445545
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4669635292605997
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.439788621719315
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.44615433269461197
            name: Cosine Map@100

SentenceTransformer based on mixedbread-ai/deepset-mxbai-embed-de-large-v1

This is a sentence-transformers model finetuned from mixedbread-ai/deepset-mxbai-embed-de-large-v1 on the json dataset. It maps sentences & paragraphs to a 1024-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: mixedbread-ai/deepset-mxbai-embed-de-large-v1
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- json

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': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("FareedKhan/mixedbread-ai_deepset-mxbai-embed-de-large-v1_FareedKhan_prime_synthetic_data_2k_3_8")
# Run inference
sentences = [
    '\nThe list you provided seems to be a mix of various chemical substances, some of which appear to be medications, others are chemical compounds, and a few could be substances from other fields (e.g., water treatment, food additives). To be more precise, it would be helpful to categorize them properly based on their common usage:\n\n### Medications and Drugs:\n- **Antibiotics**: Cefoxitin, Tobramycin, Amikacin\n- ** pain and inflammation relievers**: Benoxaprofen, Daptomycin, Ceftolozane, Salicylates (Benzydamine, Dexamethasone sodium phosphate)\n- **Intravenous fluids**: Magnesium trisilicate\n- **Antivirals**: Ribavirin, Inotersen\n- **Antibacterial agents**: Epirizole, Floctafenine, Flunixin\n- **Vaccines**: Vaborbactam, Brincidofovir, Adefovir\n- **Neuromodulators**: Cefatrizine, Bumadizone, Alminoprofen\n- **Cancer treatments**: Colistin, Nitrofurantoin, Sisomicin\n\n### Chemical Compounds:\n- **Salts and salts of acidity**: Fosfomycin, Azosemide, Mofebutazone\n- **Amino acids**: Phenylalanine, Nitrosalicylic',
    'Which drugs interact with the SERPINA1 gene/protein as carriers?',
    'Is there a regulatory function associated with the epidermal growth factor receptor or its interacting proteins in the control of genes or proteins that participate in the inactivation of fast sodium channels during Phase 1 of cardiac action potential propagation?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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	0.3911
cosine_accuracy@3	0.4752
cosine_accuracy@5	0.495
cosine_accuracy@10	0.5545
cosine_precision@1	0.3911
cosine_precision@3	0.1584
cosine_precision@5	0.099
cosine_precision@10	0.0554
cosine_recall@1	0.3911
cosine_recall@3	0.4752
cosine_recall@5	0.495
cosine_recall@10	0.5545
cosine_ndcg@10	0.467
cosine_mrr@10	0.4398
cosine_map@100	0.4462

Training Details

Training Dataset

json

Dataset: json
Size: 1,814 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 3 tokens
mean: 267.06 tokens
max: 512 tokens

min: 15 tokens
mean: 39.66 tokens
max: 120 tokens

	positive	anchor
type	string	string
details	min: 3 tokens mean: 267.06 tokens max: 512 tokens	min: 15 tokens mean: 39.66 tokens max: 120 tokens

Samples:

positive	anchor
Based on the provided information, it appears you are describing a complex biological system involving various molecules, drugs, diseases, and anatomical structures. Here's a breakdown: ### Key Entities 1. Molecules and Targets - Mentioned molecules include metabolites, phenols, and drugs, with specific functional groups related to their chemical properties. - Targets include enzymes (like acetyl-CoA carboxylase) and diseases causing various health conditions (e.g., Finnish type amyloidosis, lung cancer). 2. Functionality and Interactions - The molecules and drugs interact with various biological processes, pathways, and bodily systems.	`Identify common genetic targets that interact with both N-(3,5-dibromo-4-hydroxyphenyl)benzamide and 1-Naphthylamine-5-sulfonic acid.`
The provided list appears to be a collection of gene symbols related to cancer. Gene symbols are used in genetics and molecular biology to identify genes. Each symbol is associated with a specific gene that plays a role in cellular functions, including cancer processes. When studying cancer, researchers often analyze these genes to understand their roles in tumor development, potential as targets for therapy, or as indicators for patient prognosis. For example, some genes listed are known oncogenes or tumor suppressor genes: - TP53: A tumor suppressor gene that when mutated can lead to uncontrolled cell growth. - P53, POLD1, PTEN: These are well-known tumor suppressors that help regulate cell division and DNA repair. - BRCA	`Which anatomical structures lack expression of genes or proteins involved in the homogentisate degradation pathway?`
The gene in question appears to have a wide range of functions across various biological processes and body systems. It's involved in several key areas that regulate cellular responses, metabolic processes, and organ development. Here is a summary of its potential roles: 1. Cell Growth and Regulation: The gene contributes to growth control in cells, particularly in smooth muscle cells, and seems to influence cell proliferation, which is essential for tissue repair and development. 2. Nerve Function: It plays a role in functions like signal transduction, neurotrophin signaling, and regulation of neural activity, suggesting it’s involved in neural health and development. 3. Metabolic Processes: There is evidence linking	`Identify genes or proteins that interact with angiotensin-converting enzyme 2 (ACE2) and are linked to a common phenotype or effect.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768
    ],
    "matryoshka_weights": [
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
learning_rate: 1e-05
warmup_ratio: 0.1
bf16: True
tf32: False
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 8
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: 1e-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: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
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
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_map@100
0	0	-	0.3930
0.0441	10	1.18	-
0.0881	20	1.0507	-
0.1322	30	0.9049	-
0.1762	40	0.8999	-
0.2203	50	0.6519	-
0.2643	60	0.5479	-
0.3084	70	0.6493	-
0.3524	80	0.4706	-
0.3965	90	0.5459	-
0.4405	100	0.5692	-
0.4846	110	0.7834	-
0.5286	120	0.5341	-
0.5727	130	0.5343	-
0.6167	140	0.4865	-
0.6608	150	0.3942	-
0.7048	160	0.3578	-
0.7489	170	0.5158	-
0.7930	180	0.3426	-
0.8370	190	0.5789	-
0.8811	200	0.5271	-
0.9251	210	0.577	-
0.9692	220	0.5193	-
1.0	227	-	0.4354
1.0132	230	0.4598	-
1.0573	240	0.2735	-
1.1013	250	0.2919	-
1.1454	260	0.3206	-
1.1894	270	0.2851	-
1.2335	280	0.3899	-
1.2775	290	0.3279	-
1.3216	300	0.2155	-
1.3656	310	0.3471	-
1.4097	320	0.327	-
1.4537	330	0.229	-
1.4978	340	0.2902	-
1.5419	350	0.3216	-
1.5859	360	0.2902	-
1.6300	370	0.4527	-
1.6740	380	0.1583	-
1.7181	390	0.3144	-
1.7621	400	0.2573	-
1.8062	410	0.2309	-
1.8502	420	0.3475	-
1.8943	430	0.3082	-
1.9383	440	0.3176	-
1.9824	450	0.2104	-
2.0	454	-	0.4453
2.0264	460	0.2615	-
2.0705	470	0.1599	-
2.1145	480	0.1015	-
2.1586	490	0.2154	-
2.2026	500	0.1161	-
2.2467	510	0.2208	-
2.2907	520	0.2035	-
2.3348	530	0.1622	-
2.3789	540	0.1758	-
2.4229	550	0.2782	-
2.4670	560	0.303	-
2.5110	570	0.1787	-
2.5551	580	0.2221	-
2.5991	590	0.1686	-
2.6432	600	0.2522	-
2.6872	610	0.1334	-
2.7313	620	0.1102	-
2.7753	630	0.2499	-
2.8194	640	0.2648	-
2.8634	650	0.1859	-
2.9075	660	0.2385	-
2.9515	670	0.2283	-
2.9956	680	0.1126	-
3.0	681	-	0.4462

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.10
Sentence Transformers: 3.1.1
Transformers: 4.45.1
PyTorch: 2.2.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.1
Tokenizers: 0.20.0

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}
}