--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:132553 - loss:MultipleNegativesSymmetricRankingLoss base_model: laion/clap-htsat-fused widget: - source_sentence: HE WAS OUT OF HIS MIND WITH SOMETHING HE OVERHEARD ABOUT EATING PEOPLE'S FLESH AND DRINKING BLOOD WHAT'S THE GOOD OF TALKING LIKE THAT sentences: - NESTORIUS WHO DEPENDED ON THE NEAR APPROACH OF HIS EASTERN FRIENDS PERSISTED LIKE HIS PREDECESSOR CHRYSOSTOM TO DISCLAIM THE JURISDICTION AND TO DISOBEY THE SUMMONS OF HIS ENEMIES THEY HASTENED HIS TRIAL AND HIS ACCUSER PRESIDED IN THE SEAT OF JUDGMENT - THEN BACK I TURNED MY FACE TO THOSE HIGH THINGS WHICH MOVED THEMSELVES TOWARDS US SO SEDATELY THEY HAD BEEN DISTANCED BY NEW WEDDED BRIDES - THE PROGRESS OF PRESIDENT DAVIS TO THE NEW CAPITAL SET IN THE VERY FACE OF THE FOE WAS TO BE ONE HUGE TRIUMPH OF FAITH AND LOYALTY - source_sentence: I BELIEVE THE SERIOUSNESS OF THE AMERICANS ARISES PARTLY FROM THEIR PRIDE sentences: - YOU HAVE BEEN TO THE HOTEL HE BURST OUT YOU HAVE SEEN CATHERINE - WHAT DO YOU MEAN SIR - A HARSH LAUGH FROM COMRADE OSSIPON CUT THE TIRADE DEAD SHORT IN A SUDDEN FALTERING OF THE TONGUE AND A BEWILDERED UNSTEADINESS OF THE APOSTLE'S MILDLY EXALTED EYES - source_sentence: BUT YOU OUGHT TO HAVE KNOWN THAT WE ARE ONLY HALF AN HOUR BEHIND YOU AT SYDENHAM IN THE MATTER OF NEWS sentences: - DOWN BELOW IN THE QUIET NARROW STREET MEASURED FOOTSTEPS APPROACHED THE HOUSE THEN DIED AWAY UNHURRIED AND FIRM AS IF THE PASSER BY HAD STARTED TO PACE OUT ALL ETERNITY FROM GAS LAMP TO GAS LAMP IN A NIGHT WITHOUT END AND THE DROWSY TICKING OF THE OLD CLOCK ON THE LANDING BECAME DISTINCTLY AUDIBLE IN THE BEDROOM - IT WAS A SUMMER NIGHT AND THE GUESTS WERE WANDERING IN AND OUT AT WILL AND THROUGH HOUSE AND GARDEN AMID LOVELY THINGS OF ALL COLORS AND ODORS - IF A MAN WERE SLAIN IN BATTLE IT WAS AN OLD CUSTOM TO PLACE HIS BODY AGAINST A TREE OR ROCK IN A SITTING POSITION ALWAYS FACING THE ENEMY TO INDICATE HIS UNDAUNTED DEFIANCE AND BRAVERY EVEN IN DEATH - source_sentence: THE MERCHANT'S DAUGHTER AT FIRST DID NOT ANSWER BUT AS HE KEPT ON CALLING TO HER SHE FINALLY ASKED HIM WHAT IT WAS THAT HE WANTED sentences: - LODGED IN THE BRANCHES OF A PINYON TREE I THINK IT IS BUT HE DOESN'T ANSWER ME - HOW ASKED TAD - THE SECOND WAS AS IF HER FLESH AND BONES HAD ALL BEEN FASHIONED OUT OF EMERALD THE THIRD APPEARED AS SNOW BUT NEWLY FALLEN - source_sentence: THERE ARE NATURES TOO TO WHOSE SENSE OF JUSTICE THE PRICE EXACTED LOOMS UP MONSTROUSLY ENORMOUS ODIOUS OPPRESSIVE WORRYING HUMILIATING EXTORTIONATE INTOLERABLE THOSE ARE THE FANATICS sentences: - I SHALL LOCK UP ALL THE DOORS AND WINDOWS IN THE HOUSE AND THEN I SHALL GIVE YOU MY LATCH KEY AND YOU CAN LET YOURSELF IN AND STAY THE NIGHT HERE THERE IS NO ONE IN THE HOUSE - 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 - HE BEGAN TO WISH THAT HE HAD COMPROMISED IN SOME WAY OR OTHER THAT HE HAD SENT THE MONEY PERHAPS HE COULD DO IT UP HERE 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: 114.78151570511905 energy_consumed: 0.42889417052827883 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: 2.094 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: 0.108 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.196 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.272 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.438 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.108 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.06533333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.054400000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0438 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.108 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.196 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.272 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.438 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.24322279069515917 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.18493690476190464 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20597911270433167 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: librispeech test type: librispeech-test metrics: - type: cosine_accuracy@1 value: 0.151 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.288 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.371 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.518 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.151 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.096 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.0742 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0518 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.151 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.288 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.371 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.518 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.31319206378414244 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.25047857142857116 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2693786295421681 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-fused](https://huggingface.co/laion/clap-htsat-fused) on the [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr) 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 - **Base model:** [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) - **Maximum Sequence Length:** None tokens - **Output Dimensionality:** None dimensions - **Similarity Function:** Cosine Similarity - **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/UKPLab/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': None}, 'audio': {'method': 'get_audio_features', 'method_output_name': None}}, 'module_output_name': 'sentence_embedding', '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-fused-librispeech") # Run inference sentences = [ 'THERE ARE NATURES TOO TO WHOSE SENSE OF JUSTICE THE PRICE EXACTED LOOMS UP MONSTROUSLY ENORMOUS ODIOUS OPPRESSIVE WORRYING HUMILIATING EXTORTIONATE INTOLERABLE THOSE ARE THE FANATICS', 'HE BEGAN TO WISH THAT HE HAD COMPROMISED IN SOME WAY OR OTHER THAT HE HAD SENT THE MONEY PERHAPS HE COULD DO IT UP HERE', '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', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, -0.4742, -0.2719], # [-0.4742, 1.0000, 0.8206], # [-0.2719, 0.8206, 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.evaluation.InformationRetrievalEvaluator) | Metric | librispeech-eval | librispeech-test | |:--------------------|:-----------------|:-----------------| | cosine_accuracy@1 | 0.108 | 0.151 | | cosine_accuracy@3 | 0.196 | 0.288 | | cosine_accuracy@5 | 0.272 | 0.371 | | cosine_accuracy@10 | 0.438 | 0.518 | | cosine_precision@1 | 0.108 | 0.151 | | cosine_precision@3 | 0.0653 | 0.096 | | cosine_precision@5 | 0.0544 | 0.0742 | | cosine_precision@10 | 0.0438 | 0.0518 | | cosine_recall@1 | 0.108 | 0.151 | | cosine_recall@3 | 0.196 | 0.288 | | cosine_recall@5 | 0.272 | 0.371 | | cosine_recall@10 | 0.438 | 0.518 | | **cosine_ndcg@10** | **0.2432** | **0.3132** | | cosine_mrr@10 | 0.1849 | 0.2505 | | cosine_map@100 | 0.206 | 0.2694 | ## 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: 132,553 training samples * Columns: audio and text * Approximate statistics based on the first 1000 samples: | | audio | text | |:--------|:-------------------|:-------------------------------------------------------------------------------------------------| | type | dict | string | | details | | | * Samples: | audio | text | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | {'path': '374-180298-0000.flac', 'array': array([ 6.92203816e-04, 8.04404495e-04, 8.03834875e-04, ...,
-3.02505396e-05, -6.59527450e-06, 1.11444592e-06]), 'sampling_rate': 48000} | 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 | | {'path': '374-180298-0001.flac', 'array': array([-9.33515839e-05, -1.25754057e-04, -1.44482241e-04, ...,
-2.66165182e-04, -2.03228556e-04, -1.03404833e-04]), 'sampling_rate': 48000} | 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 | | {'path': '374-180298-0002.flac', 'array': array([-2.47883319e-04, -2.91854434e-04, -2.82971043e-04, ...,
-1.43931946e-04, -1.17829914e-04, -6.32331648e-05]), 'sampling_rate': 48000} | 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: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### 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: 1,000 evaluation samples * Columns: audio and text * Approximate statistics based on the first 1000 samples: | | audio | text | |:--------|:-------------------|:------------------------------------------------------------------------------------------------| | type | dict | string | | details | | | * Samples: | audio | text | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | {'path': '2277-149896-0000.flac', 'array': array([ 0.00179741, 0.00170625, 0.00120927, ..., -0.00144462,
-0.00102732, -0.00048062]), 'sampling_rate': 48000} | HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE | | {'path': '2277-149896-0001.flac', 'array': array([ 0.00111104, 0.00081758, 0.00021103, ..., -0.00138193,
-0.0009173 , -0.00041702]), 'sampling_rate': 48000} | 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 | | {'path': '2277-149896-0002.flac', 'array': array([0.00080266, 0.00088462, 0.00083408, ..., 0.00105488, 0.00083673,
0.00043296]), 'sampling_rate': 48000} | HURSTWOOD WALKED THE FLOOR MENTALLY ARRANGING THE CHIEF POINTS OF HIS SITUATION | * Loss: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### 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`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### 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 - `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`: 1 - `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 - `use_cpu`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `bf16`: True - `fp16`: False - `half_precision_backend`: None - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `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_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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 - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `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`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: no - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: True - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | librispeech-eval_cosine_ndcg@10 | librispeech-test_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:-------------------------------:| | -1 | -1 | - | - | 0.0114 | - | | 0.0100 | 83 | 3.5908 | - | - | - | | 0.0200 | 166 | 2.5371 | - | - | - | | 0.0301 | 249 | 2.1799 | - | - | - | | 0.0401 | 332 | 2.0415 | - | - | - | | 0.0501 | 415 | 1.9394 | - | - | - | | 0.0601 | 498 | 1.8167 | - | - | - | | 0.0701 | 581 | 1.7589 | - | - | - | | 0.0801 | 664 | 1.7262 | - | - | - | | 0.0902 | 747 | 1.7585 | - | - | - | | 0.1001 | 829 | - | 1.5991 | 0.0335 | - | | 0.1002 | 830 | 1.7521 | - | - | - | | 0.1102 | 913 | 1.6822 | - | - | - | | 0.1202 | 996 | 1.6176 | - | - | - | | 0.1302 | 1079 | 1.6391 | - | - | - | | 0.1403 | 1162 | 1.6931 | - | - | - | | 0.1503 | 1245 | 1.4626 | - | - | - | | 0.1603 | 1328 | 1.4305 | - | - | - | | 0.1703 | 1411 | 1.4998 | - | - | - | | 0.1803 | 1494 | 1.4073 | - | - | - | | 0.1903 | 1577 | 1.3843 | - | - | - | | 0.2001 | 1658 | - | 1.2227 | 0.0925 | - | | 0.2004 | 1660 | 1.3371 | - | - | - | | 0.2104 | 1743 | 1.3908 | - | - | - | | 0.2204 | 1826 | 1.2835 | - | - | - | | 0.2304 | 1909 | 1.3203 | - | - | - | | 0.2404 | 1992 | 1.2549 | - | - | - | | 0.2505 | 2075 | 1.2384 | - | - | - | | 0.2605 | 2158 | 1.2189 | - | - | - | | 0.2705 | 2241 | 1.1658 | - | - | - | | 0.2805 | 2324 | 1.1771 | - | - | - | | 0.2905 | 2407 | 1.2068 | - | - | - | | 0.3002 | 2487 | - | 1.0471 | 0.1318 | - | | 0.3005 | 2490 | 1.1708 | - | - | - | | 0.3106 | 2573 | 1.1389 | - | - | - | | 0.3206 | 2656 | 1.0786 | - | - | - | | 0.3306 | 2739 | 1.0792 | - | - | - | | 0.3406 | 2822 | 1.0562 | - | - | - | | 0.3506 | 2905 | 0.98 | - | - | - | | 0.3607 | 2988 | 1.1153 | - | - | - | | 0.3707 | 3071 | 0.9987 | - | - | - | | 0.3807 | 3154 | 1.0002 | - | - | - | | 0.3907 | 3237 | 1.0017 | - | - | - | | 0.4002 | 3316 | - | 0.8901 | 0.1589 | - | | 0.4007 | 3320 | 0.9364 | - | - | - | | 0.4107 | 3403 | 0.9394 | - | - | - | | 0.4208 | 3486 | 0.9459 | - | - | - | | 0.4308 | 3569 | 0.9604 | - | - | - | | 0.4408 | 3652 | 0.9491 | - | - | - | | 0.4508 | 3735 | 0.9295 | - | - | - | | 0.4608 | 3818 | 0.9508 | - | - | - | | 0.4709 | 3901 | 0.9122 | - | - | - | | 0.4809 | 3984 | 0.8483 | - | - | - | | 0.4909 | 4067 | 0.8443 | - | - | - | | 0.5003 | 4145 | - | 0.7955 | 0.1908 | - | | 0.5009 | 4150 | 0.8838 | - | - | - | | 0.5109 | 4233 | 0.8367 | - | - | - | | 0.5209 | 4316 | 0.8516 | - | - | - | | 0.5310 | 4399 | 0.8112 | - | - | - | | 0.5410 | 4482 | 0.8368 | - | - | - | | 0.5510 | 4565 | 0.873 | - | - | - | | 0.5610 | 4648 | 0.8156 | - | - | - | | 0.5710 | 4731 | 0.8864 | - | - | - | | 0.5811 | 4814 | 0.8278 | - | - | - | | 0.5911 | 4897 | 0.8006 | - | - | - | | 0.6004 | 4974 | - | 0.7649 | 0.1874 | - | | 0.6011 | 4980 | 0.8199 | - | - | - | | 0.6111 | 5063 | 0.7475 | - | - | - | | 0.6211 | 5146 | 0.7345 | - | - | - | | 0.6311 | 5229 | 0.7301 | - | - | - | | 0.6412 | 5312 | 0.774 | - | - | - | | 0.6512 | 5395 | 0.7391 | - | - | - | | 0.6612 | 5478 | 0.6929 | - | - | - | | 0.6712 | 5561 | 0.7218 | - | - | - | | 0.6812 | 5644 | 0.7071 | - | - | - | | 0.6912 | 5727 | 0.7024 | - | - | - | | 0.7004 | 5803 | - | 0.6712 | 0.2419 | - | | 0.7013 | 5810 | 0.6428 | - | - | - | | 0.7113 | 5893 | 0.6719 | - | - | - | | 0.7213 | 5976 | 0.6972 | - | - | - | | 0.7313 | 6059 | 0.7043 | - | - | - | | 0.7413 | 6142 | 0.663 | - | - | - | | 0.7514 | 6225 | 0.6963 | - | - | - | | 0.7614 | 6308 | 0.6591 | - | - | - | | 0.7714 | 6391 | 0.6736 | - | - | - | | 0.7814 | 6474 | 0.7033 | - | - | - | | 0.7914 | 6557 | 0.6314 | - | - | - | | 0.8005 | 6632 | - | 0.6806 | 0.2319 | - | | 0.8014 | 6640 | 0.6508 | - | - | - | | 0.8115 | 6723 | 0.6532 | - | - | - | | 0.8215 | 6806 | 0.6788 | - | - | - | | 0.8315 | 6889 | 0.6038 | - | - | - | | 0.8415 | 6972 | 0.658 | - | - | - | | 0.8515 | 7055 | 0.656 | - | - | - | | 0.8616 | 7138 | 0.6533 | - | - | - | | 0.8716 | 7221 | 0.601 | - | - | - | | 0.8816 | 7304 | 0.6243 | - | - | - | | 0.8916 | 7387 | 0.6315 | - | - | - | | 0.9005 | 7461 | - | 0.6526 | 0.2432 | - | | 0.9016 | 7470 | 0.5707 | - | - | - | | 0.9116 | 7553 | 0.5778 | - | - | - | | 0.9217 | 7636 | 0.5736 | - | - | - | | 0.9317 | 7719 | 0.615 | - | - | - | | 0.9417 | 7802 | 0.5756 | - | - | - | | 0.9517 | 7885 | 0.5724 | - | - | - | | 0.9617 | 7968 | 0.5678 | - | - | - | | 0.9718 | 8051 | 0.5661 | - | - | - | | 0.9818 | 8134 | 0.6162 | - | - | - | | 0.9918 | 8217 | 0.5766 | - | - | - | | -1 | -1 | - | - | - | 0.3132 |
### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.429 kWh - **Carbon Emitted**: 0.115 kg of CO2 - **Hours Used**: 2.094 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: 5.2.0.dev0 - Transformers: 4.57.0.dev0 - PyTorch: 2.8.0+cu128 - Accelerate: 1.6.0 - Datasets: 3.6.0 - Tokenizers: 0.22.1 ## 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", } ```