--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:28539 - loss:MultipleNegativesRankingLoss base_model: laion/clap-htsat-unfused widget: - source_sentence: HE DECIDED TO WRITE HER CARE OF THE WEST SIDE POST OFFICE AND ASK FOR AN EXPLANATION AS WELL AS TO HAVE HER MEET HIM sentences: - GRADUALLY RELIEF CAME TO ALL OF US - IT SEEMED AS IF HIS FAMILY TROUBLES WERE JUST BEGINNING - I EXPLAINED TO ANTONIA HOW THIS MEANT THAT HE WAS TWENTY FOUR YEARS OLD THAT HE MUST HAVE BEEN THERE WHEN WHITE MEN FIRST CAME LEFT ON FROM BUFFALO AND INDIAN TIMES - source_sentence: WITHOUT A WORD PETER GOT UP AND LIT HIS LANTERN sentences: - AS LEADING TO THE MENTION OF OTHER INTERESTING EVENTS WE MUST SET THIS INROAD CLEARLY BEFORE THE READER - SHE WANTED TO MAKE SOME REFERENCE TO THEIR RELATIONS UPON THE TRAIN BUT WAS TOO TIMID - THE DISTINGUISHING MARK OF THE HENS WAS A CREST OF LAMENTABLY SCANTY GROWTH IN THESE LATTER DAYS BUT SO ODDLY AND WICKEDLY ANALOGOUS TO HEPZIBAH'S TURBAN THAT PHOEBE TO THE POIGNANT DISTRESS OF HER CONSCIENCE BUT INEVITABLY WAS LED TO FANCY A GENERAL RESEMBLANCE BETWIXT THESE FORLORN BIPEDS AND HER RESPECTABLE RELATIVE - source_sentence: NOTHING COULD BE MORE NATURAL THAN SUCH AN ASSEMBLY IN SUCH A PLACE AT SUCH A PERIOD sentences: - BUT HE COMPROMISED BY TELLING THE BOY THAT THERE WOULD BE NO REPLY - MANY LITTLE WRINKLES GATHERED BETWEEN HIS EYES AS HE CONTEMPLATED THIS AND HIS BROW MOISTENED - HE DID MANAGE TO BRING HIMSELF INTO THE MOOD TO GO OUT TO CARRIE BUT WHEN HE GOT IN OGDEN PLACE HE THOUGHT HE SAW A MAN WATCHING HIM AND WENT AWAY - source_sentence: DEAR SIR WE BEG TO INFORM YOU THAT WE ARE INSTRUCTED TO WAIT UNTIL TO MORROW THURSDAY AT ONE O'CLOCK BEFORE FILING SUIT AGAINST YOU ON BEHALF OF MISSUS JULIA HURSTWOOD FOR DIVORCE AND ALIMONY sentences: - THE WHITE DOUBLE ROSEBUSH HAD EVIDENTLY BEEN PROPPED UP ANEW AGAINST THE HOUSE SINCE THE COMMENCEMENT OF THE SEASON AND A PEAR TREE AND THREE DAMSON TREES WHICH EXCEPT A ROW OF CURRANT BUSHES CONSTITUTED THE ONLY VARIETIES OF FRUIT BORE MARKS OF THE RECENT AMPUTATION OF SEVERAL SUPERFLUOUS OR DEFECTIVE LIMBS - LASTLY THE ROYAL BROTHERS FELL THEMSELVES VICTIMS TO THE EPIDEMIC WHICH SO SADLY SIGNALIZES THEIR REIGN - IT IS LIKE A BANDAGE OVER ONE'S EYES TO COME INTO IT - source_sentence: HERE THE HOLY PRELATE OF FERNS MET HIM AND RELATED A VISION IN WHICH HE HAD BEEN INSTRUCTED TO DEMAND THE ABOLITION OF THE IMPOST sentences: - THE SHARP SMELL OF SPIRITS WENT THROUGH THE ROOM - YES HOW MANY - QUICKLY IT WAS COVERED WITH BRIGHT RED SPOTS I THOUGHT I HAD NEVER SEEN ANY BLOOD SO BRIGHT datasets: - openslr/librispeech_asr pipeline_tag: sentence-similarity 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 co2_eq_emissions: emissions: 578.4000971210925 energy_consumed: 2.161257658642011 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 7.59 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: CLAP model trained on COCO Captions results: - task: type: information-retrieval name: Information Retrieval dataset: name: librispeech eval type: librispeech-eval metrics: - type: cosine_accuracy@1 value: 0.245 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.645 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.785 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.245 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12899999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0785 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.245 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.52 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.645 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.785 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.503027364772325 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.41403968253968265 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4252888359623941 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: librispeech test type: librispeech-test metrics: - type: cosine_accuracy@1 value: 0.04885496183206107 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1183206106870229 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.16908396946564885 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.2641221374045801 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.04885496183206107 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.03944020356234096 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.033816793893129776 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.026412213740458015 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04885496183206107 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1183206106870229 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.16908396946564885 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2641221374045801 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1402219692077291 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.10268266085059953 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.11950657997396778 name: Cosine Map@100 --- # CLAP model trained on COCO Captions This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) on the [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) dataset. It maps sentences & paragraphs to a 512-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:** [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 512 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modalities:** Text, Audio - **Training Dataset:** - [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': 'pooler_output'}, 'audio': {'method': 'get_audio_features', 'method_output_name': 'pooler_output'}}, 'module_output_name': 'sentence_embedding', 'message_format': 'auto', 'architecture': 'ClapModel'}) ) ``` ## 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("tomaarsen/clap-htsat-unfused-librispeech-5-epochs-128bs") # Run inference inputs = [ 'https://huggingface.co/tomaarsen/clap-htsat-unfused-librispeech-5-epochs-128bs/resolve/main/assets/audio_0.wav', 'https://huggingface.co/tomaarsen/clap-htsat-unfused-librispeech-5-epochs-128bs/resolve/main/assets/audio_1.wav', 'https://huggingface.co/tomaarsen/clap-htsat-unfused-librispeech-5-epochs-128bs/resolve/main/assets/audio_2.wav', ] embeddings = model.encode(inputs) print(embeddings.shape) # [3, 512] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.4362, 0.6843], # [0.4362, 1.0000, 0.2179], # [0.6843, 0.2179, 1.0000]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `librispeech-eval` and `librispeech-test` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) | Metric | librispeech-eval | librispeech-test | |:--------------------|:-----------------|:-----------------| | cosine_accuracy@1 | 0.245 | 0.0489 | | cosine_accuracy@3 | 0.52 | 0.1183 | | cosine_accuracy@5 | 0.645 | 0.1691 | | cosine_accuracy@10 | 0.785 | 0.2641 | | cosine_precision@1 | 0.245 | 0.0489 | | cosine_precision@3 | 0.1733 | 0.0394 | | cosine_precision@5 | 0.129 | 0.0338 | | cosine_precision@10 | 0.0785 | 0.0264 | | cosine_recall@1 | 0.245 | 0.0489 | | cosine_recall@3 | 0.52 | 0.1183 | | cosine_recall@5 | 0.645 | 0.1691 | | cosine_recall@10 | 0.785 | 0.2641 | | **cosine_ndcg@10** | **0.503** | **0.1402** | | cosine_mrr@10 | 0.414 | 0.1027 | | cosine_map@100 | 0.4253 | 0.1195 | ## Training Details ### Training Dataset #### librispeech_asr * Dataset: [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) at [71cacbf](https://huggingface.co/datasets/openslr/librispeech_asr/tree/71cacbfb7e2354c4226d01e70d77d5fca3d04ba1) * Size: 28,539 training samples * Columns: audio and text * Approximate statistics based on the first 1000 samples: | | audio | text | |:--------|:------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | audio | string | | details | | | * Samples: | audio | text | |:-------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | | CHAPTER SIXTEEN I MIGHT HAVE TOLD YOU OF THE BEGINNING OF THIS LIAISON IN A FEW LINES BUT I WANTED YOU TO SEE EVERY STEP BY WHICH WE CAME I TO AGREE TO WHATEVER MARGUERITE WISHED | | | MARGUERITE TO BE UNABLE TO LIVE APART FROM ME IT WAS THE DAY AFTER THE EVENING WHEN SHE CAME TO SEE ME THAT I SENT HER MANON LESCAUT FROM THAT TIME SEEING THAT I COULD NOT CHANGE MY MISTRESS'S LIFE I CHANGED MY OWN | | | I WISHED ABOVE ALL NOT TO LEAVE MYSELF TIME TO THINK OVER THE POSITION I HAD ACCEPTED FOR IN SPITE OF MYSELF IT WAS A GREAT DISTRESS TO ME THUS MY LIFE GENERALLY SO CALM | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false, "directions": [ "query_to_doc", "doc_to_query" ], "partition_mode": "per_direction", "hardness_mode": null, "hardness_strength": 0.0 } ``` ### Evaluation Dataset #### librispeech_asr * Dataset: [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) at [71cacbf](https://huggingface.co/datasets/openslr/librispeech_asr/tree/71cacbfb7e2354c4226d01e70d77d5fca3d04ba1) * Size: 200 evaluation samples * Columns: audio and text * Approximate statistics based on the first 200 samples: | | audio | text | |:--------|:-----------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | audio | string | | details | | | * Samples: | audio | text | |:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | | HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE | | | HE WOULD HAVE TO PAY HER THE MONEY WHICH SHE WOULD NOW REGULARLY DEMAND OR THERE WOULD BE TROUBLE IT DID NOT MATTER WHAT HE DID | | | HURSTWOOD WALKED THE FLOOR MENTALLY ARRANGING THE CHIEF POINTS OF HIS SITUATION | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false, "directions": [ "query_to_doc", "doc_to_query" ], "partition_mode": "per_direction", "hardness_mode": null, "hardness_strength": 0.0 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 4 - `num_train_epochs`: 5 - `learning_rate`: 2e-05 - `warmup_steps`: 0.1 - `bf16`: True - `eval_strategy`: steps - `per_device_eval_batch_size`: 4 - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `per_device_train_batch_size`: 4 - `num_train_epochs`: 5 - `max_steps`: -1 - `learning_rate`: 2e-05 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_steps`: 0.1 - `optim`: adamw_torch_fused - `optim_args`: None - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `optim_target_modules`: None - `gradient_accumulation_steps`: 1 - `average_tokens_across_devices`: True - `max_grad_norm`: 1.0 - `label_smoothing_factor`: 0.0 - `bf16`: True - `fp16`: False - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `use_liger_kernel`: False - `liger_kernel_config`: None - `use_cache`: False - `neftune_noise_alpha`: None - `torch_empty_cache_steps`: None - `auto_find_batch_size`: False - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `include_num_input_tokens_seen`: no - `log_level`: passive - `log_level_replica`: warning - `disable_tqdm`: False - `project`: huggingface - `trackio_space_id`: trackio - `eval_strategy`: steps - `per_device_eval_batch_size`: 4 - `prediction_loss_only`: True - `eval_on_start`: False - `eval_do_concat_batches`: True - `eval_use_gather_object`: False - `eval_accumulation_steps`: None - `include_for_metrics`: [] - `batch_eval_metrics`: False - `save_only_model`: False - `save_on_each_node`: False - `enable_jit_checkpoint`: False - `push_to_hub`: False - `hub_private_repo`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_always_push`: False - `hub_revision`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `restore_callback_states_from_checkpoint`: False - `full_determinism`: False - `seed`: 42 - `data_seed`: None - `use_cpu`: False - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `dataloader_prefetch_factor`: None - `remove_unused_columns`: True - `label_names`: None - `train_sampling_strategy`: random - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `ddp_backend`: None - `ddp_timeout`: 1800 - `fsdp`: [] - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `deepspeed`: None - `debug`: [] - `skip_memory_metrics`: True - `do_predict`: False - `resume_from_checkpoint`: None - `warmup_ratio`: None - `local_rank`: -1 - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | librispeech-eval_cosine_ndcg@10 | librispeech-test_cosine_ndcg@10 | |:------:|:-----:|:-------------:|:---------------:|:-------------------------------:|:-------------------------------:| | -1 | -1 | - | - | 0.0279 | 0.0037 | | 0.1001 | 714 | 1.4538 | 1.1503 | 0.0727 | - | | 0.2001 | 1428 | 0.9953 | 0.8749 | 0.0841 | - | | 0.3002 | 2142 | 0.9557 | 0.7760 | 0.1252 | - | | 0.4003 | 2856 | 0.9621 | 2.4026 | 0.0353 | - | | 0.5004 | 3570 | 0.9721 | 0.9326 | 0.0720 | - | | 0.6004 | 4284 | 0.8931 | 0.8454 | 0.0934 | - | | 0.7005 | 4998 | 0.8368 | 0.5494 | 0.1741 | - | | 0.8006 | 5712 | 0.8001 | 0.4935 | 0.2170 | - | | 0.9006 | 6426 | 0.7817 | 0.7168 | 0.1476 | - | | 1.0007 | 7140 | 0.7235 | 0.6410 | 0.1809 | - | | 1.1008 | 7854 | 0.6620 | 0.6527 | 0.1726 | - | | 1.2008 | 8568 | 0.6492 | 0.4146 | 0.2116 | - | | 1.3009 | 9282 | 0.6342 | 0.7536 | 0.1695 | - | | 1.4010 | 9996 | 0.6438 | 0.6872 | 0.1873 | - | | 1.5011 | 10710 | 0.6103 | 0.4385 | 0.2767 | - | | 1.6011 | 11424 | 0.6052 | 0.8028 | 0.1805 | - | | 1.7012 | 12138 | 0.5950 | 0.3628 | 0.2891 | - | | 1.8013 | 12852 | 0.5672 | 0.6978 | 0.2120 | - | | 1.9013 | 13566 | 0.5611 | 0.5946 | 0.1965 | - | | 2.0014 | 14280 | 0.5546 | 0.2659 | 0.3589 | - | | 2.1015 | 14994 | 0.5133 | 0.4273 | 0.2806 | - | | 2.2015 | 15708 | 0.4588 | 0.4356 | 0.2929 | - | | 2.3016 | 16422 | 0.4629 | 0.5123 | 0.2538 | - | | 2.4017 | 17136 | 0.4429 | 0.3757 | 0.3092 | - | | 2.5018 | 17850 | 0.5000 | 0.4237 | 0.3297 | - | | 2.6018 | 18564 | 0.4328 | 0.5146 | 0.3291 | - | | 2.7019 | 19278 | 0.4284 | 0.3348 | 0.3483 | - | | 2.8020 | 19992 | 0.4598 | 0.3768 | 0.3865 | - | | 2.9020 | 20706 | 0.4183 | 0.3908 | 0.2594 | - | | 3.0021 | 21420 | 0.4180 | 0.3240 | 0.3470 | - | | 3.1022 | 22134 | 0.3624 | 0.3487 | 0.4205 | - | | 3.2022 | 22848 | 0.3627 | 0.3124 | 0.3650 | - | | 3.3023 | 23562 | 0.3651 | 0.3025 | 0.3046 | - | | 3.4024 | 24276 | 0.3644 | 0.3708 | 0.4050 | - | | 3.5025 | 24990 | 0.3480 | 0.3458 | 0.3998 | - | | 3.6025 | 25704 | 0.3542 | 0.2936 | 0.4141 | - | | 3.7026 | 26418 | 0.2954 | 0.2692 | 0.3876 | - | | 3.8027 | 27132 | 0.3336 | 0.2221 | 0.3915 | - | | 3.9027 | 27846 | 0.3255 | 0.3140 | 0.4253 | - | | 4.0028 | 28560 | 0.3093 | 0.2278 | 0.4607 | - | | 4.1029 | 29274 | 0.2715 | 0.3176 | 0.4261 | - | | 4.2029 | 29988 | 0.2812 | 0.2814 | 0.4590 | - | | 4.3030 | 30702 | 0.2690 | 0.2390 | 0.4997 | - | | 4.4031 | 31416 | 0.2697 | 0.2575 | 0.4720 | - | | 4.5032 | 32130 | 0.2616 | 0.3054 | 0.4863 | - | | 4.6032 | 32844 | 0.2437 | 0.2467 | 0.4852 | - | | 4.7033 | 33558 | 0.2532 | 0.2505 | 0.5196 | - | | 4.8034 | 34272 | 0.2640 | 0.2242 | 0.4926 | - | | 4.9034 | 34986 | 0.2245 | 0.2345 | 0.4999 | - | | -1 | -1 | - | - | 0.5030 | 0.1402 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 2.161 kWh - **Carbon Emitted**: 0.578 kg of CO2 - **Hours Used**: 7.59 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 5.4.0.dev0 - Transformers: 5.3.0.dev0 - PyTorch: 2.10.0+cu128 - Accelerate: 1.13.0.dev0 - Datasets: 4.3.0 - Tokenizers: 0.22.2 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{günther2024jinaembeddings28192token, title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao}, year={2024}, eprint={2310.19923}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2310.19923}, } ```