--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1027471 - loss:CoSENTLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 datasets: [] metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: watter dag is gedenkteken dag die jaar sentences: - request news - turn volume down - request news - source_sentence: wat is die week se weertoestand sentences: - play radio - make coffee - traffic query - source_sentence: skakel aan die roomba sentences: - tell joke - start cleaning - request datetime - source_sentence: kan jy my 'n goeie grap vertel sentences: - set alarm - play music - tell joke - source_sentence: vertel my die huidige tyd in ottawa sentences: - set alarm - request definition - query cooking pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: MiniLM dev type: MiniLM-dev metrics: - type: pearson_cosine value: 0.8464008477003933 name: Pearson Cosine - type: spearman_cosine value: 0.8128883563290172 name: Spearman Cosine - type: pearson_manhattan value: 0.8204825552661638 name: Pearson Manhattan - type: spearman_manhattan value: 0.8069612779979122 name: Spearman Manhattan - type: pearson_euclidean value: 0.8207664286968728 name: Pearson Euclidean - type: spearman_euclidean value: 0.806851537985582 name: Spearman Euclidean - type: pearson_dot value: 0.7927608791449223 name: Pearson Dot - type: spearman_dot value: 0.8078229606916496 name: Spearman Dot - type: pearson_max value: 0.8464008477003933 name: Pearson Max - type: spearman_max value: 0.8128883563290172 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: MiniLM test type: MiniLM-test metrics: - type: pearson_cosine value: 0.9079517679775697 name: Pearson Cosine - type: spearman_cosine value: 0.842595786650747 name: Spearman Cosine - type: pearson_manhattan value: 0.885352838846903 name: Pearson Manhattan - type: spearman_manhattan value: 0.8389283098138718 name: Spearman Manhattan - type: pearson_euclidean value: 0.8858228063346806 name: Pearson Euclidean - type: spearman_euclidean value: 0.8390847286161828 name: Spearman Euclidean - type: pearson_dot value: 0.8618645999355777 name: Pearson Dot - type: spearman_dot value: 0.8389938584674199 name: Spearman Dot - type: pearson_max value: 0.9079517679775697 name: Pearson Max - type: spearman_max value: 0.842595786650747 name: Spearman Max --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 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': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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("philipp-zettl/MiniLM-amazon_massive_intent-similarity") # Run inference sentences = [ 'vertel my die huidige tyd in ottawa', 'query cooking', 'request definition', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `MiniLM-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8464 | | **spearman_cosine** | **0.8129** | | pearson_manhattan | 0.8205 | | spearman_manhattan | 0.807 | | pearson_euclidean | 0.8208 | | spearman_euclidean | 0.8069 | | pearson_dot | 0.7928 | | spearman_dot | 0.8078 | | pearson_max | 0.8464 | | spearman_max | 0.8129 | #### Semantic Similarity * Dataset: `MiniLM-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.908 | | **spearman_cosine** | **0.8426** | | pearson_manhattan | 0.8854 | | spearman_manhattan | 0.8389 | | pearson_euclidean | 0.8858 | | spearman_euclidean | 0.8391 | | pearson_dot | 0.8619 | | spearman_dot | 0.839 | | pearson_max | 0.908 | | spearman_max | 0.8426 | ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: 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`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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 - `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`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | loss | MiniLM-dev_spearman_cosine | MiniLM-test_spearman_cosine | |:------:|:-----:|:-------------:|:------:|:--------------------------:|:---------------------------:| | 0.0031 | 100 | 7.4879 | - | - | - | | 0.0062 | 200 | 6.4531 | - | - | - | | 0.0093 | 300 | 6.4185 | - | - | - | | 0.0125 | 400 | 4.5043 | - | - | - | | 0.0156 | 500 | 5.1274 | - | - | - | | 0.0187 | 600 | 6.0006 | - | - | - | | 0.0218 | 700 | 4.8066 | - | - | - | | 0.0249 | 800 | 3.9536 | - | - | - | | 0.0280 | 900 | 4.7259 | - | - | - | | 0.0311 | 1000 | 3.7583 | 2.6440 | 0.6640 | - | | 0.0343 | 1100 | 3.9905 | - | - | - | | 0.0374 | 1200 | 4.8914 | - | - | - | | 0.0405 | 1300 | 3.895 | - | - | - | | 0.0436 | 1400 | 3.1582 | - | - | - | | 0.0467 | 1500 | 3.7172 | - | - | - | | 0.0498 | 1600 | 3.6785 | - | - | - | | 0.0529 | 1700 | 3.9632 | - | - | - | | 0.0561 | 1800 | 3.9643 | - | - | - | | 0.0592 | 1900 | 2.829 | - | - | - | | 0.0623 | 2000 | 2.5923 | 2.3344 | 0.7459 | - | | 0.0654 | 2100 | 3.1617 | - | - | - | | 0.0685 | 2200 | 2.6366 | - | - | - | | 0.0716 | 2300 | 4.3751 | - | - | - | | 0.0747 | 2400 | 3.4732 | - | - | - | | 0.0779 | 2500 | 2.5695 | - | - | - | | 0.0810 | 2600 | 2.7479 | - | - | - | | 0.0841 | 2700 | 2.5274 | - | - | - | | 0.0872 | 2800 | 2.4204 | - | - | - | | 0.0903 | 2900 | 4.1305 | - | - | - | | 0.0934 | 3000 | 4.091 | 2.0951 | 0.7426 | - | | 0.0965 | 3100 | 3.7972 | - | - | - | | 0.0997 | 3200 | 2.6029 | - | - | - | | 0.1028 | 3300 | 3.2422 | - | - | - | | 0.1059 | 3400 | 3.3747 | - | - | - | | 0.1090 | 3500 | 3.3358 | - | - | - | | 0.1121 | 3600 | 2.8658 | - | - | - | | 0.1152 | 3700 | 2.6436 | - | - | - | | 0.1183 | 3800 | 2.2006 | - | - | - | | 0.1215 | 3900 | 2.0549 | - | - | - | | 0.1246 | 4000 | 2.4642 | 3.4108 | 0.7236 | - | | 0.1277 | 4100 | 2.9219 | - | - | - | | 0.1308 | 4200 | 2.6581 | - | - | - | | 0.1339 | 4300 | 2.2697 | - | - | - | | 0.1370 | 4400 | 2.7215 | - | - | - | | 0.1401 | 4500 | 2.6023 | - | - | - | | 0.1433 | 4600 | 1.8772 | - | - | - | | 0.1464 | 4700 | 2.6885 | - | - | - | | 0.1495 | 4800 | 2.6005 | - | - | - | | 0.1526 | 4900 | 1.4849 | - | - | - | | 0.1557 | 5000 | 2.4896 | 3.4860 | 0.7117 | - | | 0.1588 | 5100 | 2.6038 | - | - | - | | 0.1619 | 5200 | 2.0584 | - | - | - | | 0.1651 | 5300 | 1.9156 | - | - | - | | 0.1682 | 5400 | 1.467 | - | - | - | | 0.1713 | 5500 | 0.5799 | - | - | - | | 0.1744 | 5600 | 1.617 | - | - | - | | 0.1775 | 5700 | 1.3764 | - | - | - | | 0.1806 | 5800 | 3.067 | - | - | - | | 0.1837 | 5900 | 2.2463 | - | - | - | | 0.1869 | 6000 | 1.5466 | 2.5326 | 0.7721 | - | | 0.1900 | 6100 | 1.4097 | - | - | - | | 0.1931 | 6200 | 1.7852 | - | - | - | | 0.1962 | 6300 | 1.2715 | - | - | - | | 0.1993 | 6400 | 2.5585 | - | - | - | | 0.2024 | 6500 | 2.4665 | - | - | - | | 0.2055 | 6600 | 1.7246 | - | - | - | | 0.2087 | 6700 | 1.145 | - | - | - | | 0.2118 | 6800 | 1.614 | - | - | - | | 0.2149 | 6900 | 1.7206 | - | - | - | | 0.2180 | 7000 | 2.6349 | 2.6824 | 0.7652 | - | | 0.2211 | 7100 | 2.1896 | - | - | - | | 0.2242 | 7200 | 1.9106 | - | - | - | | 0.2274 | 7300 | 1.3783 | - | - | - | | 0.2305 | 7400 | 0.7119 | - | - | - | | 0.2336 | 7500 | 1.5037 | - | - | - | | 0.2367 | 7600 | 1.8365 | - | - | - | | 0.2398 | 7700 | 1.3817 | - | - | - | | 0.2429 | 7800 | 1.7101 | - | - | - | | 0.2460 | 7900 | 1.6716 | - | - | - | | 0.2492 | 8000 | 1.3013 | 3.5864 | 0.7401 | - | | 0.2523 | 8100 | 1.5131 | - | - | - | | 0.2554 | 8200 | 2.3699 | - | - | - | | 0.2585 | 8300 | 1.6179 | - | - | - | | 0.2616 | 8400 | 1.3 | - | - | - | | 0.2647 | 8500 | 1.5151 | - | - | - | | 0.2678 | 8600 | 2.8703 | - | - | - | | 0.2710 | 8700 | 2.5076 | - | - | - | | 0.2741 | 8800 | 1.9876 | - | - | - | | 0.2772 | 8900 | 1.5823 | - | - | - | | 0.2803 | 9000 | 1.0845 | 2.4197 | 0.7833 | - | | 0.2834 | 9100 | 1.2871 | - | - | - | | 0.2865 | 9200 | 1.3901 | - | - | - | | 0.2896 | 9300 | 1.1607 | - | - | - | | 0.2928 | 9400 | 2.1171 | - | - | - | | 0.2959 | 9500 | 1.4335 | - | - | - | | 0.2990 | 9600 | 0.801 | - | - | - | | 0.3021 | 9700 | 1.4567 | - | - | - | | 0.3052 | 9800 | 1.7046 | - | - | - | | 0.3083 | 9900 | 1.4378 | - | - | - | | 0.3114 | 10000 | 2.3191 | 2.3063 | 0.7903 | - | | 0.3146 | 10100 | 1.6518 | - | - | - | | 0.3177 | 10200 | 0.9857 | - | - | - | | 0.3208 | 10300 | 2.2052 | - | - | - | | 0.3239 | 10400 | 2.0443 | - | - | - | | 0.3270 | 10500 | 2.08 | - | - | - | | 0.3301 | 10600 | 2.0009 | - | - | - | | 0.3332 | 10700 | 1.3274 | - | - | - | | 0.3364 | 10800 | 1.0298 | - | - | - | | 0.3395 | 10900 | 1.7127 | - | - | - | | 0.3426 | 11000 | 1.3371 | 4.0607 | 0.7211 | - | | 0.3457 | 11100 | 2.7555 | - | - | - | | 0.3488 | 11200 | 4.1792 | - | - | - | | 0.3519 | 11300 | 2.0931 | - | - | - | | 0.3550 | 11400 | 2.4591 | - | - | - | | 0.3582 | 11500 | 3.4962 | - | - | - | | 0.3613 | 11600 | 1.9228 | - | - | - | | 0.3644 | 11700 | 2.7295 | - | - | - | | 0.3675 | 11800 | 1.5425 | - | - | - | | 0.3706 | 11900 | 1.1586 | - | - | - | | 0.3737 | 12000 | 1.1336 | 2.2959 | 0.7890 | - | | 0.3768 | 12100 | 1.572 | - | - | - | | 0.3800 | 12200 | 1.2827 | - | - | - | | 0.3831 | 12300 | 1.6352 | - | - | - | | 0.3862 | 12400 | 1.4708 | - | - | - | | 0.3893 | 12500 | 1.4719 | - | - | - | | 0.3924 | 12600 | 1.4136 | - | - | - | | 0.3955 | 12700 | 1.3969 | - | - | - | | 0.3986 | 12800 | 1.7228 | - | - | - | | 0.4018 | 12900 | 4.2842 | - | - | - | | 0.4049 | 13000 | 3.5861 | 2.1113 | 0.7956 | - | | 0.4080 | 13100 | 2.9718 | - | - | - | | 0.4111 | 13200 | 3.1554 | - | - | - | | 0.4142 | 13300 | 3.1357 | - | - | - | | 0.4173 | 13400 | 2.8488 | - | - | - | | 0.4204 | 13500 | 3.7433 | - | - | - | | 0.4236 | 13600 | 2.4195 | - | - | - | | 0.4267 | 13700 | 2.1384 | - | - | - | | 0.4298 | 13800 | 2.7965 | - | - | - | | 0.4329 | 13900 | 1.7869 | - | - | - | | 0.4360 | 14000 | 3.0356 | 2.7234 | 0.7697 | - | | 0.4391 | 14100 | 3.4984 | - | - | - | | 0.4422 | 14200 | 2.4959 | - | - | - | | 0.4454 | 14300 | 2.4615 | - | - | - | | 0.4485 | 14400 | 2.6309 | - | - | - | | 0.4516 | 14500 | 1.9831 | - | - | - | | 0.4547 | 14600 | 3.25 | - | - | - | | 0.4578 | 14700 | 3.3112 | - | - | - | | 0.4609 | 14800 | 1.9912 | - | - | - | | 0.4640 | 14900 | 1.9252 | - | - | - | | 0.4672 | 15000 | 2.4545 | 2.0730 | 0.7972 | - | | 0.4703 | 15100 | 1.6943 | - | - | - | | 0.4734 | 15200 | 2.2851 | - | - | - | | 0.4765 | 15300 | 2.4327 | - | - | - | | 0.4796 | 15400 | 1.3503 | - | - | - | | 0.4827 | 15500 | 1.1419 | - | - | - | | 0.4858 | 15600 | 1.7906 | - | - | - | | 0.4890 | 15700 | 1.6504 | - | - | - | | 0.4921 | 15800 | 1.6908 | - | - | - | | 0.4952 | 15900 | 3.0954 | - | - | - | | 0.4983 | 16000 | 1.7151 | 2.0042 | 0.8044 | - | | 0.5014 | 16100 | 1.5165 | - | - | - | | 0.5045 | 16200 | 2.5573 | - | - | - | | 0.5076 | 16300 | 1.3401 | - | - | - | | 0.5108 | 16400 | 2.5464 | - | - | - | | 0.5139 | 16500 | 2.4564 | - | - | - | | 0.5170 | 16600 | 2.1667 | - | - | - | | 0.5201 | 16700 | 1.2402 | - | - | - | | 0.5232 | 16800 | 1.932 | - | - | - | | 0.5263 | 16900 | 1.1811 | - | - | - | | 0.5294 | 17000 | 2.2014 | 2.0475 | 0.8062 | - | | 0.5326 | 17100 | 2.6535 | - | - | - | | 0.5357 | 17200 | 1.8715 | - | - | - | | 0.5388 | 17300 | 1.9385 | - | - | - | | 0.5419 | 17400 | 2.0398 | - | - | - | | 0.5450 | 17500 | 1.3436 | - | - | - | | 0.5481 | 17600 | 2.0687 | - | - | - | | 0.5512 | 17700 | 1.6224 | - | - | - | | 0.5544 | 17800 | 1.0539 | - | - | - | | 0.5575 | 17900 | 1.1162 | - | - | - | | 0.5606 | 18000 | 1.6334 | 2.4120 | 0.7964 | - | | 0.5637 | 18100 | 1.247 | - | - | - | | 0.5668 | 18200 | 2.4652 | - | - | - | | 0.5699 | 18300 | 1.8593 | - | - | - | | 0.5730 | 18400 | 1.1875 | - | - | - | | 0.5762 | 18500 | 2.1173 | - | - | - | | 0.5793 | 18600 | 1.7473 | - | - | - | | 0.5824 | 18700 | 2.1865 | - | - | - | | 0.5855 | 18800 | 1.683 | - | - | - | | 0.5886 | 18900 | 1.6522 | - | - | - | | 0.5917 | 19000 | 1.0526 | 2.0743 | 0.8033 | - | | 0.5948 | 19100 | 1.5001 | - | - | - | | 0.5980 | 19200 | 1.2606 | - | - | - | | 0.6011 | 19300 | 1.0597 | - | - | - | | 0.6042 | 19400 | 1.8603 | - | - | - | | 0.6073 | 19500 | 1.4883 | - | - | - | | 0.6104 | 19600 | 0.6594 | - | - | - | | 0.6135 | 19700 | 0.9557 | - | - | - | | 0.6166 | 19800 | 0.8651 | - | - | - | | 0.6198 | 19900 | 1.0326 | - | - | - | | 0.6229 | 20000 | 1.2785 | 2.0868 | 0.8075 | - | | 0.6260 | 20100 | 1.2881 | - | - | - | | 0.6291 | 20200 | 0.5919 | - | - | - | | 0.6322 | 20300 | 1.69 | - | - | - | | 0.6353 | 20400 | 1.0285 | - | - | - | | 0.6385 | 20500 | 0.8843 | - | - | - | | 0.6416 | 20600 | 1.3756 | - | - | - | | 0.6447 | 20700 | 0.9646 | - | - | - | | 0.6478 | 20800 | 0.8052 | - | - | - | | 0.6509 | 20900 | 0.8996 | - | - | - | | 0.6540 | 21000 | 1.2207 | 2.2881 | 0.8029 | - | | 0.6571 | 21100 | 1.3225 | - | - | - | | 0.6603 | 21200 | 1.8101 | - | - | - | | 0.6634 | 21300 | 0.8756 | - | - | - | | 0.6665 | 21400 | 0.9877 | - | - | - | | 0.6696 | 21500 | 1.7329 | - | - | - | | 0.6727 | 21600 | 1.6885 | - | - | - | | 0.6758 | 21700 | 1.2132 | - | - | - | | 0.6789 | 21800 | 1.4888 | - | - | - | | 0.6821 | 21900 | 1.403 | - | - | - | | 0.6852 | 22000 | 0.5995 | 2.1952 | 0.8036 | - | | 0.6883 | 22100 | 0.9658 | - | - | - | | 0.6914 | 22200 | 1.1485 | - | - | - | | 0.6945 | 22300 | 1.089 | - | - | - | | 0.6976 | 22400 | 1.2719 | - | - | - | | 0.7007 | 22500 | 0.9611 | - | - | - | | 0.7039 | 22600 | 0.9398 | - | - | - | | 0.7070 | 22700 | 0.7931 | - | - | - | | 0.7101 | 22800 | 1.1224 | - | - | - | | 0.7132 | 22900 | 2.032 | - | - | - | | 0.7163 | 23000 | 1.3664 | 2.1043 | 0.8075 | - | | 0.7194 | 23100 | 0.7777 | - | - | - | | 0.7225 | 23200 | 0.9427 | - | - | - | | 0.7257 | 23300 | 0.8846 | - | - | - | | 0.7288 | 23400 | 1.0039 | - | - | - | | 0.7319 | 23500 | 0.9344 | - | - | - | | 0.7350 | 23600 | 1.3712 | - | - | - | | 0.7381 | 23700 | 0.8039 | - | - | - | | 0.7412 | 23800 | 1.0735 | - | - | - | | 0.7443 | 23900 | 0.9851 | - | - | - | | 0.7475 | 24000 | 1.8673 | 2.1547 | 0.8066 | - | | 0.7506 | 24100 | 5.5805 | - | - | - | | 0.7537 | 24200 | 4.1286 | - | - | - | | 0.7568 | 24300 | 2.2206 | - | - | - | | 0.7599 | 24400 | 3.6468 | - | - | - | | 0.7630 | 24500 | 2.9307 | - | - | - | | 0.7661 | 24600 | 3.8745 | - | - | - | | 0.7693 | 24700 | 2.2125 | - | - | - | | 0.7724 | 24800 | 2.3844 | - | - | - | | 0.7755 | 24900 | 1.5081 | - | - | - | | 0.7786 | 25000 | 1.5982 | 1.8491 | 0.8145 | - | | 0.7817 | 25100 | 2.1563 | - | - | - | | 0.7848 | 25200 | 1.8558 | - | - | - | | 0.7879 | 25300 | 2.2087 | - | - | - | | 0.7911 | 25400 | 2.3953 | - | - | - | | 0.7942 | 25500 | 1.4072 | - | - | - | | 0.7973 | 25600 | 1.4637 | - | - | - | | 0.8004 | 25700 | 2.2037 | - | - | - | | 0.8035 | 25800 | 1.6241 | - | - | - | | 0.8066 | 25900 | 1.4882 | - | - | - | | 0.8097 | 26000 | 0.9108 | 1.9292 | 0.8115 | - | | 0.8129 | 26100 | 0.9198 | - | - | - | | 0.8160 | 26200 | 1.2981 | - | - | - | | 0.8191 | 26300 | 1.0513 | - | - | - | | 0.8222 | 26400 | 1.389 | - | - | - | | 0.8253 | 26500 | 5.8539 | - | - | - | | 0.8284 | 26600 | 3.547 | - | - | - | | 0.8315 | 26700 | 2.3285 | - | - | - | | 0.8347 | 26800 | 2.8112 | - | - | - | | 0.8378 | 26900 | 3.3717 | - | - | - | | 0.8409 | 27000 | 2.5921 | 1.9430 | 0.8108 | - | | 0.8440 | 27100 | 1.5048 | - | - | - | | 0.8471 | 27200 | 1.5 | - | - | - | | 0.8502 | 27300 | 0.778 | - | - | - | | 0.8533 | 27400 | 0.9557 | - | - | - | | 0.8565 | 27500 | 1.347 | - | - | - | | 0.8596 | 27600 | 1.5882 | - | - | - | | 0.8627 | 27700 | 1.7333 | - | - | - | | 0.8658 | 27800 | 1.5683 | - | - | - | | 0.8689 | 27900 | 0.7698 | - | - | - | | 0.8720 | 28000 | 1.2758 | 1.9704 | 0.8127 | - | | 0.8751 | 28100 | 1.3248 | - | - | - | | 0.8783 | 28200 | 1.041 | - | - | - | | 0.8814 | 28300 | 1.6066 | - | - | - | | 0.8845 | 28400 | 1.9033 | - | - | - | | 0.8876 | 28500 | 0.8781 | - | - | - | | 0.8907 | 28600 | 0.9345 | - | - | - | | 0.8938 | 28700 | 0.9209 | - | - | - | | 0.8969 | 28800 | 1.1443 | - | - | - | | 0.9001 | 28900 | 0.9522 | - | - | - | | 0.9032 | 29000 | 1.4295 | 2.0572 | 0.8111 | - | | 0.9063 | 29100 | 0.9005 | - | - | - | | 0.9094 | 29200 | 1.0024 | - | - | - | | 0.9125 | 29300 | 1.3573 | - | - | - | | 0.9156 | 29400 | 1.0805 | - | - | - | | 0.9187 | 29500 | 1.3308 | - | - | - | | 0.9219 | 29600 | 1.4853 | - | - | - | | 0.9250 | 29700 | 2.0785 | - | - | - | | 0.9281 | 29800 | 0.9283 | - | - | - | | 0.9312 | 29900 | 0.8081 | - | - | - | | 0.9343 | 30000 | 0.4223 | 2.0404 | 0.8115 | - | | 0.9374 | 30100 | 0.8565 | - | - | - | | 0.9405 | 30200 | 0.6674 | - | - | - | | 0.9437 | 30300 | 0.5499 | - | - | - | | 0.9468 | 30400 | 0.3212 | - | - | - | | 0.9499 | 30500 | 0.166 | - | - | - | | 0.9530 | 30600 | 0.1096 | - | - | - | | 0.9561 | 30700 | 0.0382 | - | - | - | | 0.9592 | 30800 | 0.2927 | - | - | - | | 0.9623 | 30900 | 0.4097 | - | - | - | | 0.9655 | 31000 | 0.5554 | 2.0068 | 0.8130 | - | | 0.9686 | 31100 | 0.5783 | - | - | - | | 0.9717 | 31200 | 0.376 | - | - | - | | 0.9748 | 31300 | 0.3469 | - | - | - | | 0.9779 | 31400 | 0.3043 | - | - | - | | 0.9810 | 31500 | 0.4023 | - | - | - | | 0.9841 | 31600 | 0.1876 | - | - | - | | 0.9873 | 31700 | 0.4473 | - | - | - | | 0.9904 | 31800 | 0.3256 | - | - | - | | 0.9935 | 31900 | 0.4875 | - | - | - | | 0.9966 | 32000 | 0.1807 | 2.0122 | 0.8129 | - | | 0.9997 | 32100 | 0.3249 | - | - | - | | 1.0 | 32109 | - | - | - | 0.8426 |
### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.33.0 - Datasets: 2.21.0 - 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", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```