SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
(1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'cls', 'include_prompt': True})
(2): Normalize({})
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("sentence_transformers_model_id")
5# Run inference
6sentences = [
7 'Variable breakpoints target PAX5 in patients with dicentric chromosomes: a model for the basis of unbalanced translocations in cancer. The search for target genes involved in unbalanced acquired chrom',
8 'Heterochromatin integrity affects chromosome reorganization after centromere dysfunction. The centromere is essential for the inheritance of genetic information on eukaryotic chromosomes. Epigenetic regulation of centromere identity has been implicated in genome stability, karyotype evolution, and speciation. However, little is known regarding the manner in which centromere dysfunction affects the chromosomal architectures. Here we show that in the fission yeast Schizosaccharomyces pombe, the conditional de',
9 'A framework for understanding and targeting residual disease in oncogene-driven solid cancers Molecular targeted therapy has the potential to dramatically improve survival in patients with cancer. However, complete and durable responses to targeted therapy are rare in individuals with advanced-stage solid cancers. Even the most effective targeted therapies generally do not induce a complete tumor response, resulting in residual disease and tumor progression that limits patient survival. We discuss the emerg',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 384]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities)
18# tensor([[1.0000, 0.5380, 0.0281],
19# [0.5380, 1.0000, 0.0000],
20# [0.0281, 0.0000, 1.0000]])sentence_0, sentence_1, and sentence_2| sentence_0 | sentence_1 | sentence_2 | |
|---|---|---|---|
| type | string | string | string |
| modality | text | text | text |
| details |
|
|
|
| sentence_0 | sentence_1 | sentence_2 |
|---|---|---|
Thioredoxin mediates oxidation-dependent phosphorylation of CRMP2 and growth cone collapse. Semaphorin3A (Sema3A) is a repulsive guidance molecule for axons, which acts by inducing growth cone collaps | Thioredoxin mediates oxidation-dependent phosphorylation of CRMP2 and growth cone collapse. Semaphorin3A (Sema3A) is a repulsive guidance molecule for axons, which acts by inducing growth cone collapse through phosphorylation of CRMP2 (collapsin response mediator protein 2). Here, we show a role for CRMP2 oxidation and thioredoxin (TRX) in the regulation of CRMP2 phosphorylation and growth cone collapse. Sema3A stimulation generated hydrogen peroxide (H2O2) through MICAL (molecule interacting with CasL) and | Differential regulation of actin microfilaments by human MICAL proteins. The Drosophila melanogaster MICAL protein is essential for the neuronal growth cone machinery that functions through plexin- and semaphorin-mediated axonal signaling. Drosophila MICAL is also involved in regulating myofilament organization and synaptic structures, and serves as an actin disassembly factor downstream of plexin-mediated axonal repulsion. In mammalian cells there are three known isoforms, MICAL1, MICAL2 and MICAL3, as wel |
A chromatin remodelling complex involved in transcription and DNA processing. The packaging of the eukaryotic genome in chromatin presents barriers that restrict the access of enzymes that process DNA | Chromatin remodelling at promoters suppresses antisense transcription Chromatin allows the eukaryotic cell to package its DNA efficiently. To understand how chromatin structure is controlled across the Saccharomyces cerevisiae genome, we have investigated the role of the ATP-dependent chromatin remodelling complex Isw2 in positioning nucleosomes. We find that Isw2 functions adjacent to promoter regions where it repositions nucleosomes at the interface between genic and intergenic sequences. Nucleosome repos | High-resolution mapping of open chromatin in the rice genome. Gene expression is controlled by the complex interaction of transcription factors binding to promoters and other regulatory DNA elements. One common characteristic of the genomic regions associated with regulatory proteins is a pronounced sensitivity to DNase I digestion. We generated genome-wide high-resolution maps of DNase I hypersensitive (DH) sites from both seedling and callus tissues of rice (Oryza sativa). Approximately 25% of the DH site |
Attention-deficit hyperactivity disorder: a category or a continuum? Genetic analysis of a large-scale twin study. OBJECTIVE To investigate heritability and continuum versus categorical approaches to | Emotion dysregulation in attention deficit hyperactivity disorder. Although it has long been recognized that many individuals with attention deficit hyperactivity disorder (ADHD) also have difficulties with emotion regulation, no consensus has been reached on how to conceptualize this clinically challenging domain. The authors examine the current literature using both quantitative and qualitative methods. Three key findings emerge. First, emotion dysregulation is prevalent in ADHD throughout the lifespan an | Rare and common variants: twenty arguments Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim",
4 "gather_across_devices": false,
5 "directions": [
6 "query_to_doc"
7 ],
8 "partition_mode": "joint",
9 "hardness_mode": null,
10 "hardness_strength": 0.0
11}per_device_train_batch_size: 32num_train_epochs: 5per_device_eval_batch_size: 32multi_dataset_batch_sampler: round_robinper_device_train_batch_size: 32num_train_epochs: 5max_steps: -1learning_rate: 5e-05lr_scheduler_type: linearlr_scheduler_kwargs: Nonewarmup_steps: 0optim: adamw_torch_fusedoptim_args: Noneweight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08optim_target_modules: Nonegradient_accumulation_steps: 1average_tokens_across_devices: Truemax_grad_norm: 1label_smoothing_factor: 0.0bf16: Falsefp16: Falsebf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Nonetorch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneuse_liger_kernel: Falseliger_kernel_config: Noneuse_cache: Falseneftune_noise_alpha: Nonetorch_empty_cache_steps: Noneauto_find_batch_size: Falselog_on_each_node: Truelogging_nan_inf_filter: Trueinclude_num_input_tokens_seen: nolog_level: passivelog_level_replica: warningdisable_tqdm: Falseproject: huggingfacetrackio_space_id: Nonetrackio_bucket_id: Nonetrackio_static_space_id: Noneper_device_eval_batch_size: 32prediction_loss_only: Trueeval_on_start: Falseeval_do_concat_batches: Trueeval_use_gather_object: Falseeval_accumulation_steps: Noneinclude_for_metrics: []batch_eval_metrics: Falsesave_only_model: Falsesave_on_each_node: Falseenable_jit_checkpoint: Falsepush_to_hub: Falsehub_private_repo: Nonehub_model_id: Nonehub_strategy: every_savehub_always_push: Falsehub_revision: Noneload_best_model_at_end: Falseignore_data_skip: Falserestore_callback_states_from_checkpoint: Falsefull_determinism: Falseseed: 42data_seed: Noneuse_cpu: Falseaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}parallelism_config: Nonedataloader_drop_last: Falsedataloader_num_workers: 0dataloader_pin_memory: Truedataloader_persistent_workers: Falsedataloader_prefetch_factor: Noneremove_unused_columns: Truelabel_names: Nonetrain_sampling_strategy: randomlength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falseddp_static_graph: Noneddp_backend: Noneddp_timeout: 1800fsdp: []fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}deepspeed: Nonedebug: []skip_memory_metrics: Truedo_predict: Falseresume_from_checkpoint: Nonewarmup_ratio: Nonelocal_rank: -1prompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robinrouter_mapping: {}learning_rate_mapping: {}| Epoch | Step | Training Loss |
|---|---|---|
| 0.3385 | 500 | 0.4742 |
| 0.6770 | 1000 | 0.3280 |
| 1.0156 | 1500 | 0.2917 |
| 1.3541 | 2000 | 0.2273 |
| 1.6926 | 2500 | 0.2052 |
| 2.0311 | 3000 | 0.2047 |
| 2.3697 | 3500 | 0.1703 |
| 2.7082 | 4000 | 0.1621 |
| 3.0467 | 4500 | 0.1573 |
| 3.3852 | 5000 | 0.1440 |
| 3.7238 | 5500 | 0.1376 |
| 4.0623 | 6000 | 0.1351 |
| 4.4008 | 6500 | 0.1283 |
| 4.7393 | 7000 | 0.1226 |
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{oord2019representationlearningcontrastivepredictive,
2 title={Representation Learning with Contrastive Predictive Coding},
3 author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
4 year={2019},
5 eprint={1807.03748},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG},
8 url={https://arxiv.org/abs/1807.03748},
9}