SentenceTransformer based on intfloat/multilingual-e5-large-instruct

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct. 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: intfloat/multilingual-e5-large-instruct
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 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': 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/just_for_testing_model")
# Run inference
sentences = [
    '\n\nThe gene in question appears to have a multifaceted role and involvement in various biological processes, diseases, and anatomical structures, with implications for both physiology and pathology. Here is a summary of its characteristics:\n\n### Function and Interactions\n- **Name**: mTORC1, a component of the mammalian target of rapamycin complex 1.\n- **Role**: Involved in regulation of membrane potential',
    'Identify genes or proteins that interact with KCNMB1 and share an associated phenotype or effect.',
    'Which solid-state medications specifically engage with the METAP2 gene/protein through direct interaction?',
]
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

Training Details

Training Dataset

Unnamed Dataset

• 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 tokensmean: 270.4 tokensmax: 512 tokens	min: 15 tokensmean: 39.58 tokensmax: 127 tokens

• Samples:

  positive	anchor
  Racecadotril, a drug from the Antidiarrheals and Intestinal Antiinflammatory/antiinfective Agents therapeutic group, targets the treatment of acute diarrhea and acute gastroenteritis. This drug falls under Alimentary Tract and Metabolism (ATC Classification) and is related to other antidiarrheals (ATC Specificity Class). Its chemical and functional group is classified under other antidiarrheals, reflecting its role in gastrointestinal health. With a molecular weight of 385.48 grams, and a topological polar surface area of 72.47, Racecadotril demonstrates specific properties that contribute to its effectiveness. The drug's inclusion in the investigational group suggests ongoing research to further validate its efficacy and safety in managing acute diarrhea. Racecadotril's diverse chemical nature, including amino acids, peptides, and sulfur-compound-based inhibitors, highlights its broad mechanism of action, which is crucial for addressing the multifaceted causes of acute diarrhea.	Could you suggest some effective medications for acute diarrhea?
  The gene KCNJ6, also known as potassium inwardly rectifying channel subfamily J member 6, is responsible for encoding a member of the G protein-coupled inwardly-rectifying potassium channel family. This type of potassium channel allows for a greater flow of potassium into the cell than out, playing a critical role in various physiological processes. The KCNJ6 gene modulates functions in cardiac cells and neuronal cells through G-protein coupled receptor stimulation. Mutations in KCNJ6 can lead to Keppen-Lubinsky Syndrome, a rare condition characterized by severe developmental delay, facial dysmorphism, and intellectual disability. In terms of its expression, the KCNJ6 gene is notably absent in the mucosal tissues of the mouth and the small intestine as indicated by its 'expression_absent' attribute. The absence of KCNJ6 in these specific anatomical areas might imply a tailored physiological function or evolutionary adaptation distinct from that in other organs where it is expressed. The gene's absence in the small intestine and mouth tissues might be crucial for ensuring optimal functionality within those specific environments, emphasizing the importance of precise gene expression in various parts of the body. Overall, the gene plays a vital role in cellular and physiological activities and its absence from certain tissues like the mucosal surfaces of the mouth and small intestine is indicative of tailored biological functions within these areas.	Which gene or protein is consistently not expressed in the mucosal tissues of the mouth and the small intestine?
  The term you're referring to seems to describe a wide range of biological entities or contexts, possibly within the framework of a scientific study or database that categorizes anatomical locations, tissues, or organs in the human body. Here's a breakdown of the classification:1. Lower Body and Tissue Levels: - Intestines (Large and Small): Refers to the large intestine (colon) and small intestine, parts of the digestive system. - Kidneys: Includes both the adult kidney and metanephros, an embryonic kidney stage. - Adrenal Gland: Part of the endocrine system related to metabolism, stress response, and more. - Liver: A primary organ involved in detoxification and metabolism. - Pancreas: Produces digestive enzymes and hormones such as insulin. - Renal Glomerulus: Tiny filtration units in the kidney. - Muscles (Various): Refers to a classification of skeletal, smooth, and cardiac muscles, each serving different bodily functions. - Bones and Connective Tissue: Includes bones, regions like the spinal cord, and structures like the heart, aorta, etc.2. Anatomical Areas: - Dorsal Root Ganglion: A cluster of neurons in the spinal cord. - Cerebral Cortex: Outer layers of the brain responsible for higher functions. - Embryonic Stages: Describes entities like the embryonic kidney (metanephros) and placenta, which are crucial during fetal development. - Reproductive Organ Systems: Such as the uterus, vagina, and mammary gland, highlighting the female reproductive system.3. Tissue Types: - Connective Tissue: Includes bones, the meninges, and fat. - Adipose Tissue: Found in various organs and is involved in energy storage and insulation.4. Impact of Exposure: The mention of "blood" might indicate the interest in studying the effects of environmental	Which genes or proteins exhibit interactions with HNRNPU, share an association with its related disease(s), and participate in the peroxisomal beta-oxidation process of fatty acid metabolism?

• 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
• per_device_train_batch_size: 2
• learning_rate: 1e-05
• num_train_epochs: 2
• warmup_ratio: 0.1
• bf16: True
• tf32: False
• load_best_model_at_end: True

All Hyperparameters

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 2
• 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: 2
• 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
• eval_use_gather_object: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.10
• Sentence Transformers: 3.0.1
• Transformers: 4.43.4
• PyTorch: 2.4.0+cu121
• Accelerate: 0.33.0
• Datasets: 2.21.0
• 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}
}