--- 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:111 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Template la - Spy cepA s3062 F30 Sequence ( 5' /3') Oligo [ l AGACTCCATATGGAGTCTAGCCAAACAG500 nM GAACA (SEQ ID NO, 1) 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 sentences: - (45) Date of Patent - http - ID - source_sentence: :-"<-------t 40000 -1-----/-f-~~-----I 35000 -----+-IN---------- § 30000 ----t+t---=~--- ~ 25000 ----~---++------t ~ 20000 -1----ff-r-ff.,.__----->t''n-\--------l sentences: - 45000 -------,-----=..... - -~' ~-- -~< - comprises - source_sentence: 55 1. A composition comprising i) a forward template comprising a nucleic acid sequence comprising a recognition region at the 3' end that is complementary to the 3' end of the Streptococcus pyogenes (S. pyogenes) cell envelope proteinase A 60 (cepA) gene antisense strand; a nicking enzyme bind­ ing site and a nicking site upstream of said recognition region; and a stabilizing region upstream of said nick­ ing site, the forward template comprising a nucleotide sequence having at least 80, 85, or 95% identity to SEQ 65 sentences: - ''' -- ,'' ,.,,,..,,,. _..,,,,.,,, .... ~-__ .... , , _,. ........-----.' - What is claimed is - annotated as follows - source_sentence: 0 1 2 3 4 5 6 7 8 9 10 Time (minutes) FIG. 1 (Cont.) sentences: - ',-;.-' - I I I I I I I I I - (21) Appl. No. - source_sentence: '~ " ''"-''-en 25000 1 ,.,,µ,· ,, · .,-,.. •~h • 1 (1) ,\ II J } 7; . \ \(9,i, .,u, 4\:' sentences: - 80, 85, or 95% identity to SEQ ID NO - u - en 25000 I ' 'lJVL' • -. • . .,.. ""~" '' ' I Q) l!J "667 7 ..._7 ... -, 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: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.07692307692307693 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.07692307692307693 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.23076923076923078 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.02564102564102564 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.015384615384615385 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.02307692307692308 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07692307692307693 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.07692307692307693 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.23076923076923078 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10157463646252407 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.06227106227106227 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08137504276350917 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.07692307692307693 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.07692307692307693 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.23076923076923078 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.02564102564102564 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.015384615384615385 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.02307692307692308 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07692307692307693 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.07692307692307693 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.23076923076923078 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.09595574046316672 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.05662393162393163 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.0744997471979569 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.07692307692307693 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.07692307692307693 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.23076923076923078 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.02564102564102564 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.015384615384615385 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.02307692307692308 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07692307692307693 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.07692307692307693 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.23076923076923078 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.0981693666921052 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.05897435897435897 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08277736107354086 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.07692307692307693 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.23076923076923078 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.23076923076923078 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.38461538461538464 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.07692307692307693 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07692307692307693 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04615384615384616 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.038461538461538464 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.07692307692307693 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.23076923076923078 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.23076923076923078 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.38461538461538464 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21938110224036803 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1700854700854701 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1860790779646314 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.07692307692307693 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.15384615384615385 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.3076923076923077 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.02564102564102564 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.03076923076923077 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.03076923076923077 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07692307692307693 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.15384615384615385 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3076923076923077 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1299580480538269 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.07628205128205127 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.10015432076692518 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./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](https://huggingface.co./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](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): 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: ```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("kr-manish/bge-base-raw_pdf_finetuned_vf1") # Run inference sentences = [ '~ " \'"-\'-en 25000 1 ,.,,µ,· ,, · .,-,.. •~h • 1 (1) ,\\ II J } 7; . \\ \\(9,i, .,u, 4\\:', 'en 25000 I \' \'lJVL\' • -. • . .,.. ""~" \'\' \' I Q) l!J "667 7 ..._7 ... -,', '80, 85, or 95% identity to SEQ ID NO', ] 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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0 | | cosine_accuracy@3 | 0.0769 | | cosine_accuracy@5 | 0.0769 | | cosine_accuracy@10 | 0.2308 | | cosine_precision@1 | 0.0 | | cosine_precision@3 | 0.0256 | | cosine_precision@5 | 0.0154 | | cosine_precision@10 | 0.0231 | | cosine_recall@1 | 0.0 | | cosine_recall@3 | 0.0769 | | cosine_recall@5 | 0.0769 | | cosine_recall@10 | 0.2308 | | cosine_ndcg@10 | 0.1016 | | cosine_mrr@10 | 0.0623 | | **cosine_map@100** | **0.0814** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0 | | cosine_accuracy@3 | 0.0769 | | cosine_accuracy@5 | 0.0769 | | cosine_accuracy@10 | 0.2308 | | cosine_precision@1 | 0.0 | | cosine_precision@3 | 0.0256 | | cosine_precision@5 | 0.0154 | | cosine_precision@10 | 0.0231 | | cosine_recall@1 | 0.0 | | cosine_recall@3 | 0.0769 | | cosine_recall@5 | 0.0769 | | cosine_recall@10 | 0.2308 | | cosine_ndcg@10 | 0.096 | | cosine_mrr@10 | 0.0566 | | **cosine_map@100** | **0.0745** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0 | | cosine_accuracy@3 | 0.0769 | | cosine_accuracy@5 | 0.0769 | | cosine_accuracy@10 | 0.2308 | | cosine_precision@1 | 0.0 | | cosine_precision@3 | 0.0256 | | cosine_precision@5 | 0.0154 | | cosine_precision@10 | 0.0231 | | cosine_recall@1 | 0.0 | | cosine_recall@3 | 0.0769 | | cosine_recall@5 | 0.0769 | | cosine_recall@10 | 0.2308 | | cosine_ndcg@10 | 0.0982 | | cosine_mrr@10 | 0.059 | | **cosine_map@100** | **0.0828** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0769 | | cosine_accuracy@3 | 0.2308 | | cosine_accuracy@5 | 0.2308 | | cosine_accuracy@10 | 0.3846 | | cosine_precision@1 | 0.0769 | | cosine_precision@3 | 0.0769 | | cosine_precision@5 | 0.0462 | | cosine_precision@10 | 0.0385 | | cosine_recall@1 | 0.0769 | | cosine_recall@3 | 0.2308 | | cosine_recall@5 | 0.2308 | | cosine_recall@10 | 0.3846 | | cosine_ndcg@10 | 0.2194 | | cosine_mrr@10 | 0.1701 | | **cosine_map@100** | **0.1861** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.0 | | cosine_accuracy@3 | 0.0769 | | cosine_accuracy@5 | 0.1538 | | cosine_accuracy@10 | 0.3077 | | cosine_precision@1 | 0.0 | | cosine_precision@3 | 0.0256 | | cosine_precision@5 | 0.0308 | | cosine_precision@10 | 0.0308 | | cosine_recall@1 | 0.0 | | cosine_recall@3 | 0.0769 | | cosine_recall@5 | 0.1538 | | cosine_recall@10 | 0.3077 | | cosine_ndcg@10 | 0.13 | | cosine_mrr@10 | 0.0763 | | **cosine_map@100** | **0.1002** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 111 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------| | ply C Tris pH8.0 Dextran Trehalose dNTPS Na2SO4 Triton X-100 DTT TABLE 3 GAS Lyophilization Mix -Reagent Composition vl.0 v2.0 Strep A (Target) Lyo Conditions 500 nM F30 500 nM F30b.5om 100 nM R41m 100 nM R41m.lb.5om 200 nM MB4 FAM 200 nM MB4_ Fam 3.0. ug 5.0 ug 30U 0.7 ug 1 ug 1 ug 50mM 50 mM Dextran 150 Dextran 500 5% in 2x Iyo 5% in 2x Iyo 100 mM in 2x Iyo 100 mM in 2x Iyo 0.3 mM 0.3 mM 15 mM 22.5 mM 0.10% 0.10% 2mM 2mM Strep A (IC) Lyo Conditions | NE | | CTGTTTG (SEQ ID NO, 5) To confirm that the targeted sequence was conserved among all GAS cepA sequences found in the public domain as well as unique to GAS, multiple sequence alignments and BLAST analyses were performed. Multiple alignment analysis of these sequences showed complete homology for the region of the gene targeted by the 3062 assay. Further, there are currently 24 complete GAS genomes (including whole genome shotgun sequence) available for sequence analysis in NCBI Genome. The cepA gene is present in all 24 genomes, and the 3062 target region within cepA is conserved among all 24 genomes. Upon BLAST analysis, it was confirmed that no other species contain significant homology to the 3062 target sequence. Assay Development As a reference, the reagent mixtures discussed below are | GCAATCTGAGGAGAGGCCATACTTGTTC | | AGATTGC (SEQ ID NO, 4) | CAAACAGGAACAAGTATGGCCTCTCCTC | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "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`: 32 - `num_train_epochs`: 15 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `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`: 32 - `eval_accumulation_steps`: None - `learning_rate`: 5e-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`: 15 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `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`: 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 - `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 | - | 0.0747 | 0.0694 | 0.0681 | 0.1224 | 0.0705 | | 1.0 | 1 | - | 0.0750 | 0.0694 | 0.0681 | 0.1224 | 0.0705 | | 2.0 | 2 | - | 0.1008 | 0.0724 | 0.0696 | 0.0719 | 0.0710 | | **3.0** | **3** | **-** | **0.1861** | **0.0828** | **0.0745** | **0.1002** | **0.0814** | | 4.0 | 4 | - | 0.1711 | 0.0968 | 0.0825 | 0.0861 | 0.1001 | | 5.0 | 6 | - | 0.1505 | 0.1140 | 0.1094 | 0.1534 | 0.1502 | | 6.0 | 7 | - | 0.1222 | 0.1143 | 0.1108 | 0.1528 | 0.1520 | | 7.0 | 8 | - | 0.1589 | 0.1536 | 0.1512 | 0.1513 | 0.1516 | | 8.0 | 9 | - | 0.1561 | 0.1550 | 0.1531 | 0.1495 | 0.1520 | | 9.0 | 10 | 1.8482 | 0.1565 | 0.1558 | 0.1544 | 0.1483 | 0.1522 | | 10.0 | 12 | - | 0.1562 | 0.1551 | 0.1557 | 0.1416 | 0.1531 | | 11.0 | 13 | - | 0.1561 | 0.1558 | 0.1562 | 0.1401 | 0.1533 | | 12.0 | 14 | - | 0.1559 | 0.1559 | 0.1562 | 0.1402 | 0.1533 | | 13.0 | 15 | - | 0.1861 | 0.0828 | 0.0745 | 0.1002 | 0.0814 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.20.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @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 ```bibtex @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} } ```