--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:353831 - loss:CosineSimilarityLoss widget: - source_sentence: A chef is preparing some food. sentences: - Five birds stand on the snow. - A chef prepared a meal. - There is no 'still' that is not relative to some other object. - source_sentence: A woman is adding oil on fishes. sentences: - Large cruise ship floating on the water. - It refers to the maximum f-stop (which is defined as the ratio of focal length to effective aperture diameter). - The woman is cutting potatoes. - source_sentence: The player shoots the winning points. sentences: - Minimum wage laws hurt the least skilled, least productive the most. - The basketball player is about to score points for his team. - Three televisions, on on the floor, the other two on a box. - source_sentence: Stars form in star-formation regions, which itself develop from molecular clouds. sentences: - Although I believe Searle is mistaken, I don't think you have found the problem. - It may be possible for a solar system like ours to exist outside of a galaxy. - A blond-haired child performing on the trumpet in front of a house while his younger brother watches. - source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign. sentences: - At first, I thought this is a bit of a tricky question. - A man plays the guitar. - There is a very good reason not to refer to the Queen's spouse as "King" - because they aren't the King. datasets: - sentence-transformers/stsb pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.7982244251277283 name: Pearson Cosine - type: spearman_cosine value: 0.8130492542348773 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.7554305375132837 name: Pearson Cosine - type: spearman_cosine value: 0.7644057551801444 name: Spearman Cosine --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. 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 - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Language:** en ### 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': 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("cahya/last-sts") # Run inference sentences = [ 'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.', 'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.', 'A man plays the guitar.', ] 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 #### Semantic Similarity * Datasets: `sts-dev` and `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | sts-dev | sts-test | |:--------------------|:----------|:-----------| | pearson_cosine | 0.7982 | 0.7554 | | **spearman_cosine** | **0.813** | **0.7644** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 353,831 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------| | A long-term researcher into diabetes, he achieved significant notability with his 1988 Banting Lecture (organized annually by the American Diabetes Association in memory of Frederick Banting). | A renowned expert on diabetes, he gained widespread acclaim for his 1988 Banting Lecture, which is presented annually by the American Diabetes Association to commemorate Frederick Banting. | 0.926345705986023 | | investigators claim the british company was a cia cover. | russian investigators stated that the british company was a cia cover. | 0.88 | | Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur football (soccer) player who competed in the 1912 Summer Olympics. | Albert Weber (21 November 1888, in Berlin – 17 September 1940) was a German amateur footballer who participated in the 1912 Summer Olympics. | 0.904914379119873 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### stsb * Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 1,500 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------|:------------------------------------------------------|:------------------| | A man with a hard hat is dancing. | A man wearing a hard hat is dancing. | 1.0 | | A young child is riding a horse. | A child is riding a horse. | 0.95 | | A man is feeding a mouse to a snake. | The man is feeding a mouse to the snake. | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `bf16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `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`: 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`: 10 - `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`: True - `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`: True - `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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:-----:|:-------------:|:---------------:|:-----------------------:|:------------------------:| | 0.0362 | 100 | 0.0019 | 0.1114 | 0.8115 | - | | 0.0724 | 200 | 0.0021 | 0.0882 | 0.8177 | - | | 0.1085 | 300 | 0.0015 | 0.0748 | 0.8125 | - | | 0.1447 | 400 | 0.0012 | 0.0679 | 0.8086 | - | | 0.1809 | 500 | 0.0012 | 0.0608 | 0.8069 | - | | 0.2171 | 600 | 0.001 | 0.0596 | 0.7986 | - | | 0.2533 | 700 | 0.0011 | 0.0547 | 0.7946 | - | | 0.2894 | 800 | 0.0011 | 0.0492 | 0.7870 | - | | 0.3256 | 900 | 0.0009 | 0.0522 | 0.7862 | - | | 0.3618 | 1000 | 0.0008 | 0.0519 | 0.7880 | - | | 0.3980 | 1100 | 0.0009 | 0.0529 | 0.7962 | - | | 0.4342 | 1200 | 0.0008 | 0.0469 | 0.7954 | - | | 0.4703 | 1300 | 0.0009 | 0.0506 | 0.7928 | - | | 0.5065 | 1400 | 0.0009 | 0.0466 | 0.7873 | - | | 0.5427 | 1500 | 0.001 | 0.0495 | 0.7999 | - | | 0.5789 | 1600 | 0.0008 | 0.0506 | 0.7861 | - | | 0.6151 | 1700 | 0.0008 | 0.0522 | 0.7873 | - | | 0.6512 | 1800 | 0.0009 | 0.0582 | 0.7843 | - | | 0.6874 | 1900 | 0.0009 | 0.0585 | 0.7888 | - | | 0.7236 | 2000 | 0.001 | 0.0508 | 0.8040 | - | | 0.7598 | 2100 | 0.001 | 0.0483 | 0.8018 | - | | 0.7959 | 2200 | 0.0008 | 0.0520 | 0.7841 | - | | 0.8321 | 2300 | 0.0009 | 0.0519 | 0.7896 | - | | 0.8683 | 2400 | 0.001 | 0.0514 | 0.7906 | - | | 0.9045 | 2500 | 0.0009 | 0.0521 | 0.7946 | - | | 0.9407 | 2600 | 0.0009 | 0.0496 | 0.7920 | - | | 0.9768 | 2700 | 0.001 | 0.0566 | 0.7956 | - | | 1.0130 | 2800 | 0.0009 | 0.0511 | 0.8044 | - | | 1.0492 | 2900 | 0.0009 | 0.0622 | 0.8197 | - | | 1.0854 | 3000 | 0.001 | 0.0504 | 0.8113 | - | | 1.1216 | 3100 | 0.001 | 0.0550 | 0.8005 | - | | 1.1577 | 3200 | 0.001 | 0.0549 | 0.7821 | - | | 1.1939 | 3300 | 0.0009 | 0.0578 | 0.7758 | - | | 1.2301 | 3400 | 0.0009 | 0.0543 | 0.7860 | - | | 1.2663 | 3500 | 0.0008 | 0.0575 | 0.7891 | - | | 1.3025 | 3600 | 0.0009 | 0.0567 | 0.7995 | - | | 1.3386 | 3700 | 0.001 | 0.0488 | 0.7985 | - | | 1.3748 | 3800 | 0.0009 | 0.0514 | 0.7789 | - | | 1.4110 | 3900 | 0.001 | 0.0584 | 0.7765 | - | | 1.4472 | 4000 | 0.001 | 0.0554 | 0.7888 | - | | 1.4834 | 4100 | 0.001 | 0.0659 | 0.7959 | - | | 1.5195 | 4200 | 0.0009 | 0.0511 | 0.7816 | - | | 1.5557 | 4300 | 0.0009 | 0.0555 | 0.7826 | - | | 1.5919 | 4400 | 0.001 | 0.0525 | 0.7944 | - | | 1.6281 | 4500 | 0.0009 | 0.0553 | 0.7941 | - | | 1.6643 | 4600 | 0.001 | 0.0588 | 0.7984 | - | | 1.7004 | 4700 | 0.001 | 0.0579 | 0.8004 | - | | 1.7366 | 4800 | 0.0009 | 0.0540 | 0.7916 | - | | 1.7728 | 4900 | 0.0009 | 0.0557 | 0.7963 | - | | 1.8090 | 5000 | 0.0008 | 0.0536 | 0.8044 | - | | 1.8452 | 5100 | 0.0009 | 0.0541 | 0.7870 | - | | 1.8813 | 5200 | 0.0009 | 0.0594 | 0.7989 | - | | 1.9175 | 5300 | 0.001 | 0.0558 | 0.8000 | - | | 1.9537 | 5400 | 0.0009 | 0.0538 | 0.7905 | - | | 1.9899 | 5500 | 0.0008 | 0.0555 | 0.7944 | - | | 2.0260 | 5600 | 0.0009 | 0.0557 | 0.8127 | - | | 2.0622 | 5700 | 0.0007 | 0.0542 | 0.8146 | - | | 2.0984 | 5800 | 0.0008 | 0.0517 | 0.7990 | - | | 2.1346 | 5900 | 0.0009 | 0.0500 | 0.8051 | - | | 2.1708 | 6000 | 0.0009 | 0.0521 | 0.8019 | - | | 2.2069 | 6100 | 0.0009 | 0.0511 | 0.8101 | - | | 2.2431 | 6200 | 0.0008 | 0.0578 | 0.8087 | - | | 2.2793 | 6300 | 0.0008 | 0.0585 | 0.8012 | - | | 2.3155 | 6400 | 0.0008 | 0.0566 | 0.8083 | - | | 2.3517 | 6500 | 0.0007 | 0.0535 | 0.8036 | - | | 2.3878 | 6600 | 0.0008 | 0.0531 | 0.7988 | - | | 2.4240 | 6700 | 0.0007 | 0.0574 | 0.8102 | - | | 2.4602 | 6800 | 0.0007 | 0.0566 | 0.7944 | - | | 2.4964 | 6900 | 0.0008 | 0.0528 | 0.8058 | - | | 2.5326 | 7000 | 0.0007 | 0.0528 | 0.8056 | - | | 2.5687 | 7100 | 0.0007 | 0.0506 | 0.8002 | - | | 2.6049 | 7200 | 0.0007 | 0.0526 | 0.8038 | - | | 2.6411 | 7300 | 0.0007 | 0.0554 | 0.8054 | - | | 2.6773 | 7400 | 0.0007 | 0.0505 | 0.7928 | - | | 2.7135 | 7500 | 0.0007 | 0.0505 | 0.8070 | - | | 2.7496 | 7600 | 0.0007 | 0.0535 | 0.7977 | - | | 2.7858 | 7700 | 0.0007 | 0.0536 | 0.8019 | - | | 2.8220 | 7800 | 0.0006 | 0.0546 | 0.7989 | - | | 2.8582 | 7900 | 0.0007 | 0.0543 | 0.8042 | - | | 2.8944 | 8000 | 0.0007 | 0.0542 | 0.8105 | - | | 2.9305 | 8100 | 0.0007 | 0.0541 | 0.8053 | - | | 2.9667 | 8200 | 0.0007 | 0.0545 | 0.8135 | - | | 3.0029 | 8300 | 0.0007 | 0.0598 | 0.8201 | - | | 3.0391 | 8400 | 0.0008 | 0.0558 | 0.8050 | - | | 3.0753 | 8500 | 0.0007 | 0.0510 | 0.7965 | - | | 3.1114 | 8600 | 0.0006 | 0.0564 | 0.8042 | - | | 3.1476 | 8700 | 0.0006 | 0.0559 | 0.7932 | - | | 3.1838 | 8800 | 0.0006 | 0.0529 | 0.8028 | - | | 3.2200 | 8900 | 0.0006 | 0.0542 | 0.8142 | - | | 3.2562 | 9000 | 0.0006 | 0.0532 | 0.8055 | - | | 3.2923 | 9100 | 0.0006 | 0.0506 | 0.7930 | - | | 3.3285 | 9200 | 0.0007 | 0.0542 | 0.7927 | - | | 3.3647 | 9300 | 0.0006 | 0.0523 | 0.8033 | - | | 3.4009 | 9400 | 0.0006 | 0.0530 | 0.8079 | - | | 3.4370 | 9500 | 0.0006 | 0.0544 | 0.7977 | - | | 3.4732 | 9600 | 0.0005 | 0.0515 | 0.8019 | - | | 3.5094 | 9700 | 0.0006 | 0.0481 | 0.8037 | - | | 3.5456 | 9800 | 0.0005 | 0.0557 | 0.8007 | - | | 3.5818 | 9900 | 0.0006 | 0.0495 | 0.8087 | - | | 3.6179 | 10000 | 0.0006 | 0.0555 | 0.7991 | - | | 3.6541 | 10100 | 0.0005 | 0.0560 | 0.7973 | - | | 3.6903 | 10200 | 0.0007 | 0.0581 | 0.7945 | - | | 3.7265 | 10300 | 0.0006 | 0.0546 | 0.8098 | - | | 3.7627 | 10400 | 0.0006 | 0.0539 | 0.8074 | - | | 3.7988 | 10500 | 0.0005 | 0.0501 | 0.8051 | - | | 3.8350 | 10600 | 0.0005 | 0.0531 | 0.8032 | - | | 3.8712 | 10700 | 0.0005 | 0.0502 | 0.8077 | - | | 3.9074 | 10800 | 0.0006 | 0.0537 | 0.8131 | - | | 3.9436 | 10900 | 0.0005 | 0.0510 | 0.8115 | - | | 3.9797 | 11000 | 0.0006 | 0.0525 | 0.8173 | - | | 4.0159 | 11100 | 0.0005 | 0.0513 | 0.8106 | - | | 4.0521 | 11200 | 0.0006 | 0.0594 | 0.8061 | - | | 4.0883 | 11300 | 0.0005 | 0.0514 | 0.8150 | - | | 4.1245 | 11400 | 0.0005 | 0.0537 | 0.8168 | - | | 4.1606 | 11500 | 0.0005 | 0.0571 | 0.8176 | - | | 4.1968 | 11600 | 0.0005 | 0.0546 | 0.8159 | - | | 4.2330 | 11700 | 0.0005 | 0.0496 | 0.8115 | - | | 4.2692 | 11800 | 0.0005 | 0.0526 | 0.8072 | - | | 4.3054 | 11900 | 0.0005 | 0.0512 | 0.8081 | - | | 4.3415 | 12000 | 0.0005 | 0.0517 | 0.8025 | - | | 4.3777 | 12100 | 0.0005 | 0.0533 | 0.8128 | - | | 4.4139 | 12200 | 0.0005 | 0.0501 | 0.8121 | - | | 4.4501 | 12300 | 0.0005 | 0.0507 | 0.8079 | - | | 4.4863 | 12400 | 0.0005 | 0.0501 | 0.8070 | - | | 4.5224 | 12500 | 0.0004 | 0.0537 | 0.8019 | - | | 4.5586 | 12600 | 0.0004 | 0.0541 | 0.8005 | - | | 4.5948 | 12700 | 0.0005 | 0.0525 | 0.8117 | - | | 4.6310 | 12800 | 0.0004 | 0.0523 | 0.8070 | - | | 4.6671 | 12900 | 0.0005 | 0.0526 | 0.8099 | - | | 4.7033 | 13000 | 0.0004 | 0.0518 | 0.8166 | - | | 4.7395 | 13100 | 0.0004 | 0.0547 | 0.8129 | - | | 4.7757 | 13200 | 0.0005 | 0.0523 | 0.8130 | - | | 4.8119 | 13300 | 0.0004 | 0.0504 | 0.8129 | - | | 4.8480 | 13400 | 0.0005 | 0.0539 | 0.8113 | - | | 4.8842 | 13500 | 0.0004 | 0.0523 | 0.8169 | - | | 4.9204 | 13600 | 0.0005 | 0.0521 | 0.8164 | - | | 4.9566 | 13700 | 0.0004 | 0.0575 | 0.8115 | - | | 4.9928 | 13800 | 0.0004 | 0.0538 | 0.8186 | - | | 5.0289 | 13900 | 0.0004 | 0.0530 | 0.8095 | - | | 5.0651 | 14000 | 0.0003 | 0.0537 | 0.8162 | - | | 5.1013 | 14100 | 0.0004 | 0.0560 | 0.8112 | - | | 5.1375 | 14200 | 0.0004 | 0.0528 | 0.8125 | - | | 5.1737 | 14300 | 0.0004 | 0.0533 | 0.8137 | - | | 5.2098 | 14400 | 0.0003 | 0.0537 | 0.8198 | - | | 5.2460 | 14500 | 0.0004 | 0.0530 | 0.8102 | - | | 5.2822 | 14600 | 0.0004 | 0.0562 | 0.8099 | - | | 5.3184 | 14700 | 0.0004 | 0.0522 | 0.8084 | - | | 5.3546 | 14800 | 0.0004 | 0.0515 | 0.8128 | - | | 5.3907 | 14900 | 0.0004 | 0.0555 | 0.8107 | - | | 5.4269 | 15000 | 0.0004 | 0.0533 | 0.8113 | - | | 5.4631 | 15100 | 0.0003 | 0.0538 | 0.8135 | - | | 5.4993 | 15200 | 0.0004 | 0.0552 | 0.8139 | - | | 5.5355 | 15300 | 0.0003 | 0.0513 | 0.8102 | - | | 5.5716 | 15400 | 0.0004 | 0.0542 | 0.8108 | - | | 5.6078 | 15500 | 0.0003 | 0.0541 | 0.8041 | - | | 5.6440 | 15600 | 0.0004 | 0.0512 | 0.8074 | - | | 5.6802 | 15700 | 0.0003 | 0.0553 | 0.8100 | - | | 5.7164 | 15800 | 0.0003 | 0.0539 | 0.8088 | - | | 5.7525 | 15900 | 0.0004 | 0.0527 | 0.8094 | - | | 5.7887 | 16000 | 0.0004 | 0.0524 | 0.8080 | - | | 5.8249 | 16100 | 0.0003 | 0.0525 | 0.8112 | - | | 5.8611 | 16200 | 0.0003 | 0.0537 | 0.8109 | - | | 5.8973 | 16300 | 0.0003 | 0.0539 | 0.8129 | - | | 5.9334 | 16400 | 0.0003 | 0.0543 | 0.8052 | - | | 5.9696 | 16500 | 0.0003 | 0.0544 | 0.8093 | - | | 6.0058 | 16600 | 0.0004 | 0.0532 | 0.8109 | - | | 6.0420 | 16700 | 0.0002 | 0.0558 | 0.8108 | - | | 6.0781 | 16800 | 0.0002 | 0.0529 | 0.8089 | - | | 6.1143 | 16900 | 0.0003 | 0.0539 | 0.8074 | - | | 6.1505 | 17000 | 0.0003 | 0.0534 | 0.8118 | - | | 6.1867 | 17100 | 0.0003 | 0.0539 | 0.8048 | - | | 6.2229 | 17200 | 0.0003 | 0.0537 | 0.8049 | - | | 6.2590 | 17300 | 0.0003 | 0.0553 | 0.8102 | - | | 6.2952 | 17400 | 0.0002 | 0.0533 | 0.8053 | - | | 6.3314 | 17500 | 0.0003 | 0.0550 | 0.8071 | - | | 6.3676 | 17600 | 0.0002 | 0.0530 | 0.8128 | - | | 6.4038 | 17700 | 0.0003 | 0.0547 | 0.8159 | - | | 6.4399 | 17800 | 0.0002 | 0.0539 | 0.8120 | - | | 6.4761 | 17900 | 0.0003 | 0.0540 | 0.8107 | - | | 6.5123 | 18000 | 0.0003 | 0.0535 | 0.8069 | - | | 6.5485 | 18100 | 0.0003 | 0.0541 | 0.8129 | - | | 6.5847 | 18200 | 0.0003 | 0.0522 | 0.8132 | - | | 6.6208 | 18300 | 0.0002 | 0.0539 | 0.8135 | - | | 6.6570 | 18400 | 0.0002 | 0.0542 | 0.8142 | - | | 6.6932 | 18500 | 0.0003 | 0.0529 | 0.8101 | - | | 6.7294 | 18600 | 0.0003 | 0.0533 | 0.8073 | - | | 6.7656 | 18700 | 0.0003 | 0.0525 | 0.8095 | - | | 6.8017 | 18800 | 0.0003 | 0.0534 | 0.8089 | - | | 6.8379 | 18900 | 0.0002 | 0.0519 | 0.8134 | - | | 6.8741 | 19000 | 0.0002 | 0.0536 | 0.8141 | - | | 6.9103 | 19100 | 0.0002 | 0.0535 | 0.8115 | - | | 6.9465 | 19200 | 0.0002 | 0.0519 | 0.8107 | - | | 6.9826 | 19300 | 0.0002 | 0.0546 | 0.8093 | - | | 7.0188 | 19400 | 0.0002 | 0.0532 | 0.8112 | - | | 7.0550 | 19500 | 0.0002 | 0.0526 | 0.8145 | - | | 7.0912 | 19600 | 0.0002 | 0.0529 | 0.8111 | - | | 7.1274 | 19700 | 0.0002 | 0.0540 | 0.8090 | - | | 7.1635 | 19800 | 0.0002 | 0.0525 | 0.8116 | - | | 7.1997 | 19900 | 0.0002 | 0.0534 | 0.8115 | - | | 7.2359 | 20000 | 0.0002 | 0.0526 | 0.8123 | - | | 7.2721 | 20100 | 0.0002 | 0.0524 | 0.8143 | - | | 7.3082 | 20200 | 0.0002 | 0.0526 | 0.8059 | - | | 7.3444 | 20300 | 0.0002 | 0.0535 | 0.8091 | - | | 7.3806 | 20400 | 0.0002 | 0.0532 | 0.8094 | - | | 7.4168 | 20500 | 0.0002 | 0.0529 | 0.8108 | - | | 7.4530 | 20600 | 0.0002 | 0.0542 | 0.8108 | - | | 7.4891 | 20700 | 0.0002 | 0.0525 | 0.8102 | - | | 7.5253 | 20800 | 0.0002 | 0.0541 | 0.8106 | - | | 7.5615 | 20900 | 0.0002 | 0.0538 | 0.8095 | - | | 7.5977 | 21000 | 0.0003 | 0.0523 | 0.8136 | - | | 7.6339 | 21100 | 0.0002 | 0.0544 | 0.8108 | - | | 7.6700 | 21200 | 0.0002 | 0.0525 | 0.8090 | - | | 7.7062 | 21300 | 0.0002 | 0.0528 | 0.8108 | - | | 7.7424 | 21400 | 0.0002 | 0.0531 | 0.8115 | - | | 7.7786 | 21500 | 0.0002 | 0.0541 | 0.8107 | - | | 7.8148 | 21600 | 0.0001 | 0.0525 | 0.8117 | - | | 7.8509 | 21700 | 0.0002 | 0.0534 | 0.8115 | - | | 7.8871 | 21800 | 0.0002 | 0.0541 | 0.8105 | - | | 7.9233 | 21900 | 0.0002 | 0.0538 | 0.8094 | - | | 7.9595 | 22000 | 0.0002 | 0.0530 | 0.8106 | - | | 7.9957 | 22100 | 0.0002 | 0.0527 | 0.8104 | - | | 8.0318 | 22200 | 0.0001 | 0.0534 | 0.8098 | - | | 8.0680 | 22300 | 0.0002 | 0.0537 | 0.8090 | - | | 8.1042 | 22400 | 0.0001 | 0.0533 | 0.8103 | - | | 8.1404 | 22500 | 0.0002 | 0.0528 | 0.8099 | - | | 8.1766 | 22600 | 0.0001 | 0.0531 | 0.8106 | - | | 8.2127 | 22700 | 0.0001 | 0.0534 | 0.8116 | - | | 8.2489 | 22800 | 0.0001 | 0.0538 | 0.8102 | - | | 8.2851 | 22900 | 0.0001 | 0.0530 | 0.8108 | - | | 8.3213 | 23000 | 0.0002 | 0.0529 | 0.8112 | - | | 8.3575 | 23100 | 0.0001 | 0.0533 | 0.8099 | - | | 8.3936 | 23200 | 0.0001 | 0.0534 | 0.8107 | - | | 8.4298 | 23300 | 0.0002 | 0.0535 | 0.8110 | - | | 8.4660 | 23400 | 0.0001 | 0.0543 | 0.8108 | - | | 8.5022 | 23500 | 0.0001 | 0.0530 | 0.8119 | - | | 8.5384 | 23600 | 0.0001 | 0.0530 | 0.8132 | - | | 8.5745 | 23700 | 0.0001 | 0.0531 | 0.8128 | - | | 8.6107 | 23800 | 0.0002 | 0.0532 | 0.8119 | - | | 8.6469 | 23900 | 0.0002 | 0.0531 | 0.8120 | - | | 8.6831 | 24000 | 0.0001 | 0.0531 | 0.8121 | - | | 8.7192 | 24100 | 0.0001 | 0.0525 | 0.8134 | - | | 8.7554 | 24200 | 0.0002 | 0.0524 | 0.8133 | - | | 8.7916 | 24300 | 0.0001 | 0.0535 | 0.8141 | - | | 8.8278 | 24400 | 0.0002 | 0.0529 | 0.8118 | - | | 8.8640 | 24500 | 0.0001 | 0.0529 | 0.8115 | - | | 8.9001 | 24600 | 0.0001 | 0.0528 | 0.8127 | - | | 8.9363 | 24700 | 0.0002 | 0.0527 | 0.8111 | - | | 8.9725 | 24800 | 0.0001 | 0.0536 | 0.8114 | - | | 9.0087 | 24900 | 0.0001 | 0.0531 | 0.8124 | - | | 9.0449 | 25000 | 0.0001 | 0.0532 | 0.8123 | - | | 9.0810 | 25100 | 0.0001 | 0.0534 | 0.8130 | - | | 9.1172 | 25200 | 0.0001 | 0.0533 | 0.8121 | - | | 9.1534 | 25300 | 0.0002 | 0.0534 | 0.8119 | - | | 9.1896 | 25400 | 0.0001 | 0.0532 | 0.8118 | - | | 9.2258 | 25500 | 0.0001 | 0.0532 | 0.8112 | - | | 9.2619 | 25600 | 0.0001 | 0.0532 | 0.8121 | - | | 9.2981 | 25700 | 0.0002 | 0.0537 | 0.8120 | - | | 9.3343 | 25800 | 0.0001 | 0.0535 | 0.8127 | - | | 9.3705 | 25900 | 0.0001 | 0.0529 | 0.8133 | - | | 9.4067 | 26000 | 0.0001 | 0.0529 | 0.8138 | - | | 9.4428 | 26100 | 0.0001 | 0.0534 | 0.8131 | - | | 9.4790 | 26200 | 0.0001 | 0.0529 | 0.8137 | - | | 9.5152 | 26300 | 0.0002 | 0.0529 | 0.8135 | - | | 9.5514 | 26400 | 0.0001 | 0.0528 | 0.8129 | - | | 9.5876 | 26500 | 0.0001 | 0.0530 | 0.8124 | - | | 9.6237 | 26600 | 0.0001 | 0.0529 | 0.8132 | - | | 9.6599 | 26700 | 0.0001 | 0.0530 | 0.8128 | - | | 9.6961 | 26800 | 0.0001 | 0.0530 | 0.8132 | - | | 9.7323 | 26900 | 0.0001 | 0.0529 | 0.8129 | - | | 9.7685 | 27000 | 0.0002 | 0.0528 | 0.8131 | - | | 9.8046 | 27100 | 0.0001 | 0.0529 | 0.8131 | - | | 9.8408 | 27200 | 0.0002 | 0.0531 | 0.8128 | - | | 9.8770 | 27300 | 0.0001 | 0.0532 | 0.8130 | - | | 9.9132 | 27400 | 0.0001 | 0.0531 | 0.8129 | - | | 9.9493 | 27500 | 0.0001 | 0.0531 | 0.8129 | - | | 9.9855 | 27600 | 0.0001 | 0.0531 | 0.8130 | - | | -1 | -1 | - | - | - | 0.7644 |
### Framework Versions - Python: 3.10.16 - Sentence Transformers: 3.4.1 - Transformers: 4.49.0 - PyTorch: 2.5.1+cu124 - Accelerate: 0.34.2 - Datasets: 2.19.2 - Tokenizers: 0.21.0 ## 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", } ```