--- language: - code tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3150 - loss:MultipleNegativesRankingLoss - loss:ContrastiveLoss widget: - source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}}, "sample_id": "census_acae7679-d077-461c-b857-ee6ccfeb267f_357"}' sentences: - Memory B cell derived from a 5-year-old human individual, with IGH + IGL, IgA1 isotype, IGHJ3*01, IGHV3-30*18, IGLC3, IGLV1-44, IGLJ3, obstructive sleep apnea, and recurrent tonsillitis. - Neuron cell type from the hippocampal formation, specifically from the Head of hippocampus (HiH) - Uncal CA1 dissection, in a 50-year-old male individual, with a supercluster term of Miscellaneous. - Prostate gland microvascular endothelial cell derived from a 74-year-old male of European ethnicity, specifically located in the transition zone of the prostate. - source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}}, "sample_id": "census_27d91086-cfe3-4e33-9282-bd1246e5ce8e_128"}' sentences: - Sample is an oligodendrocyte cell from a 29-year-old male human, with European self-reported ethnicity, specifically located in the thalamic complex. - Neuron cell type from a 50-year-old male cerebral cortex, specifically from the Long insular gyri (LIG) and Dysgranular insular cortex - Idg region, classified as Deep-layer corticothalamic and 6b. - Fibroblast cells from the thalamic complex, specifically from the medial nuclear complex of thalamus (MNC), mediodorsal nucleus of thalamus + reuniens nucleus (medioventral nucleus) of thalamus (MD + Re) in a 50-year-old male. - source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}}, "sample_id": "census_5af90777-6760-4003-9dba-8f945fec6fdf_3563"}' sentences: - Memory B cell from a 3-year-old male human with recurrent tonsillitis, expressing IgG3 isotype, IGLC2, and IGLV2-23-IGLJ2 antibody. - Macrophage cells from the kidney tissue of a female individual in her sixties, specifically SPP1+ tumor-associated macrophages (TAMs), originating from a tumor sample. - Fibroblast cells from the hypothalamus tissue, specifically from the mammillary region of HTH (HTHma) and mammillary nucleus (MN), of a 29-year-old male. - source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}}, "sample_id": "census_218acb0f-9f2f-4f76-b90b-15a4b7c7f629_30680"}' sentences: - Ependymal cell derived from the spinal cord tissue of a 50-year-old male human donor. - Progenitor cells derived from blood tissue of a 58-year old female with managed systemic lupus erythematosus (SLE). The cells are of European ethnicity and were obtained from peripheral blood mononuclear cell suspension. - Cell sample from the cortex of kidney, taken from a 46-year-old male with kidney cancer, identified as an alternatively activated macrophage. - source_sentence: '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}}, "sample_id": "census_e84f2780-51e8-4cfa-8aa0-13bbfef677c7_184"}' sentences: - A 46-year old female's liver sample, specifically conventional dendritic cell type 1 (cDC1s) enriched in CD45+ cell suspension, with no reported liver-related diseases. - Sample is an ON-bipolar cell derived from the peripheral region of the retina of a 60-year-old male with European self-reported ethnicity, mapped to GENCODE 24 reference annotation. - Alpha-beta T cell from the thoracic lymph node of a female individual in her seventies, identified as T_CD4/CD8 subtype, with predicted labels as Double-negative thymocytes, and majority voting as Regulatory T cells. datasets: - jo-mengr/geo_7k_cellxgene_3_5k_multiplets pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: SentenceTransformer results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.4942857027053833 name: Cosine Accuracy - task: type: binary-classification name: Binary Classification dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.900952380952381 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8415515422821045 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.8594377510040161 name: Cosine F1 - type: cosine_f1_threshold value: 0.7717130184173584 name: Cosine F1 Threshold - type: cosine_precision value: 0.8085642317380353 name: Cosine Precision - type: cosine_recall value: 0.9171428571428571 name: Cosine Recall - type: cosine_ap value: 0.8751864028703273 name: Cosine Ap - type: cosine_mcc value: 0.7860489453464287 name: Cosine Mcc --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained on the [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets) dataset. It maps sentences & paragraphs to a None-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 - **Maximum Sequence Length:** None tokens - **Output Dimensionality:** None dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets) - **Language:** code ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): MMContextEncoder( (text_encoder): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(28996, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSdpaSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (text_adapter): AdapterModule( (net): Sequential( (0): Linear(in_features=768, out_features=512, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=512, out_features=2048, bias=True) (3): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (omics_adapter): AdapterModule( (net): Sequential( (0): Linear(in_features=64, out_features=512, bias=True) (1): ReLU(inplace=True) (2): Linear(in_features=512, out_features=2048, bias=True) (3): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("jo-mengr/mmcontext-geo7k-cellxgene3.5k-pairs-cell_type") # Run inference sentences = [ '{"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/A2Kgip3knb4xmFj/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/HHeBR7Q9QnLM85E/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/rkHBdRGpy7qAspj/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/KXJjqrsrjnPKD3b/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/sLBtSQxQ3HxiMyE/download"}}, "sample_id": "census_e84f2780-51e8-4cfa-8aa0-13bbfef677c7_184"}', "A 46-year old female's liver sample, specifically conventional dendritic cell type 1 (cDC1s) enriched in CD45+ cell suspension, with no reported liver-related diseases.", 'Sample is an ON-bipolar cell derived from the peripheral region of the retina of a 60-year-old male with European self-reported ethnicity, mapped to GENCODE 24 reference annotation.', ] 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 #### Triplet * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.4943** | #### Binary Classification * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:-----------| | cosine_accuracy | 0.901 | | cosine_accuracy_threshold | 0.8416 | | cosine_f1 | 0.8594 | | cosine_f1_threshold | 0.7717 | | cosine_precision | 0.8086 | | cosine_recall | 0.9171 | | **cosine_ap** | **0.8752** | | cosine_mcc | 0.786 | ## Training Details ### Training Dataset #### geo_7k_cellxgene_3_5k_multiplets * Dataset: [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets) at [d5af8a2](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets/tree/d5af8a2ef144a95afa06f7294be2686f7a610e50) * Size: 3,150 training samples * Columns: anndata_ref, caption, and label * Approximate statistics based on the first 1000 samples: | | anndata_ref | caption | label | |:--------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | anndata_ref | caption | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | {"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/DCW3zXGDx6DWY7i/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbjeimYBdjefbpg/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/mggGyqZE6892DWz/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/Rt4wXwEPifBT2nX/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/dmkHbFpkJLLqHPx/download"}}, "sample_id": "census_a37f857c-779f-464e-9310-3db43a1811e7_2741"} | Sample is a macrophage cell type derived from the ileal epithelium tissue of a female human in her fourth decade. | 1.0 | | {"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/DCW3zXGDx6DWY7i/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbjeimYBdjefbpg/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/mggGyqZE6892DWz/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/Rt4wXwEPifBT2nX/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/dmkHbFpkJLLqHPx/download"}}, "sample_id": "census_a37f857c-779f-464e-9310-3db43a1811e7_2741"} | Erythrocyte cells at the mid erythroid stage, derived from bone marrow of a male human fetus at 15 weeks post-fertilization. | 0.0 | | {"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/DCW3zXGDx6DWY7i/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbjeimYBdjefbpg/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/mggGyqZE6892DWz/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/Rt4wXwEPifBT2nX/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/dmkHbFpkJLLqHPx/download"}}, "sample_id": "census_a37f857c-779f-464e-9310-3db43a1811e7_2741"} | Native cell from the spleen of a 15th week post-fertilization human female, identified as DOUBLET_IMMUNE_FIBROBLAST. | 0.0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Evaluation Dataset #### geo_7k_cellxgene_3_5k_multiplets * Dataset: [geo_7k_cellxgene_3_5k_multiplets](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets) at [d5af8a2](https://huggingface.co/datasets/jo-mengr/geo_7k_cellxgene_3_5k_multiplets/tree/d5af8a2ef144a95afa06f7294be2686f7a610e50) * Size: 350 evaluation samples * Columns: anndata_ref, caption, and label * Approximate statistics based on the first 350 samples: | | anndata_ref | caption | label | |:--------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | anndata_ref | caption | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | {"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbaoL4ydTqmYwP9/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/X8EFSis4S5ecdse/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/DGxs2PkPeDF2RGm/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/bm3N8RCWePiyJKz/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/8FGZG6EzMeBYxjX/download"}}, "sample_id": "census_b46237d1-19c6-4af2-9335-9854634bad16_7973"} | Sample contains stem cells (LGR5 stem) derived from the duodeno-jejunal junction of a human fetus at Carnegie stage 23. | 1.0 | | {"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbaoL4ydTqmYwP9/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/X8EFSis4S5ecdse/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/DGxs2PkPeDF2RGm/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/bm3N8RCWePiyJKz/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/8FGZG6EzMeBYxjX/download"}}, "sample_id": "census_b46237d1-19c6-4af2-9335-9854634bad16_7973"} | A 46-year old female's liver sample, specifically conventional dendritic cell type 1 (cDC1s) enriched in CD45+ cell suspension, with no reported liver-related diseases. | 0.0 | | {"file_record": {"dataset_path": "https://nxc-fredato.imbi.uni-freiburg.de/s/EbaoL4ydTqmYwP9/download", "embeddings": {"X_hvg": "https://nxc-fredato.imbi.uni-freiburg.de/s/X8EFSis4S5ecdse/download", "X_pca": "https://nxc-fredato.imbi.uni-freiburg.de/s/DGxs2PkPeDF2RGm/download", "X_scvi": "https://nxc-fredato.imbi.uni-freiburg.de/s/bm3N8RCWePiyJKz/download", "X_geneformer": "https://nxc-fredato.imbi.uni-freiburg.de/s/8FGZG6EzMeBYxjX/download"}}, "sample_id": "census_b46237d1-19c6-4af2-9335-9854634bad16_7973"} | A CD16-negative, CD56-bright natural killer cell sample taken from the spleen of a male in his sixth decade. | 0.0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `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`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-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`: 4 - `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`: False - `fp16`: False - `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`: False - `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 - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | cosine_ap | |:------:|:----:|:-------------:|:---------------:|:---------------:|:---------:| | -1 | -1 | - | - | 0.5029 | - | | 0.5076 | 100 | 6.008 | 9.9084 | 0.5057 | - | | 1.0152 | 200 | 4.7386 | 10.5698 | 0.4943 | - | | 0.5076 | 100 | 4.3879 | 11.5229 | 0.4943 | - | | 1.0152 | 200 | 4.1962 | 11.7110 | 0.5 | - | | 1.5228 | 300 | 4.2736 | 12.5341 | 0.4971 | - | | 2.0305 | 400 | 4.1793 | 13.1011 | 0.4943 | - | | -1 | -1 | - | - | - | 0.3408 | | 0.1692 | 100 | 0.1614 | 0.3496 | - | 0.3389 | | 0.3384 | 200 | 0.1641 | 0.3579 | - | 0.3390 | | 0.5076 | 300 | 0.1652 | 0.3592 | - | 0.3396 | | 0.6768 | 400 | 0.1672 | 0.3696 | - | 0.3413 | | 0.8460 | 500 | 0.1579 | 0.3591 | - | 0.3417 | | 1.0152 | 600 | 0.1722 | 0.2388 | - | 0.3457 | | 1.1844 | 700 | 0.1553 | 0.3597 | - | 0.3866 | | 1.3536 | 800 | 0.1029 | 0.0675 | - | 0.6485 | | 1.5228 | 900 | 0.059 | 0.0464 | - | 0.7094 | | 1.6920 | 1000 | 0.0446 | 0.0357 | - | 0.7133 | | 1.8613 | 1100 | 0.035 | 0.0286 | - | 0.7571 | | 2.0305 | 1200 | 0.0304 | 0.0226 | - | 0.8048 | | 2.1997 | 1300 | 0.0258 | 0.0293 | - | 0.7571 | | 2.3689 | 1400 | 0.0226 | 0.0179 | - | 0.8204 | | 2.5381 | 1500 | 0.0207 | 0.0160 | - | 0.8292 | | 2.7073 | 1600 | 0.0198 | 0.0166 | - | 0.8152 | | 2.8765 | 1700 | 0.0215 | 0.0157 | - | 0.8430 | | 3.0457 | 1800 | 0.0183 | 0.0161 | - | 0.8544 | | 3.2149 | 1900 | 0.0163 | 0.0138 | - | 0.8651 | | 3.3841 | 2000 | 0.0163 | 0.0142 | - | 0.8696 | | 3.5533 | 2100 | 0.0159 | 0.0129 | - | 0.8719 | | 3.7225 | 2200 | 0.015 | 0.0129 | - | 0.8773 | | 3.8917 | 2300 | 0.0157 | 0.0127 | - | 0.8752 | ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.5.0.dev0 - Transformers: 4.43.4 - PyTorch: 2.6.0 - Accelerate: 0.33.0 - Datasets: 2.14.4 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```