--- base_model: microsoft/deberta-v3-small library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:32500 - loss:GISTEmbedLoss widget: - source_sentence: phase changes do not change sentences: - The major Atlantic slave trading nations, ordered by trade volume, were the Portuguese, the British, the Spanish, the French, the Dutch, and the Danish. Several had established outposts on the African coast where they purchased slaves from local African leaders. - "phase changes do not change mass. Particles have mass, but mass is energy. \n\ \ phase changes do not change energy" - According to the U.S. Census Bureau , the county is a total area of , which has land and ( 0.2 % ) is water . - source_sentence: what jobs can you get with a bachelor degree in anthropology? sentences: - To determine the atomic weight of an element, you should add up protons and neutrons. - '[''Paleontologist*'', ''Archaeologist*'', ''University Professor*'', ''Market Research Analyst*'', ''Primatologist.'', ''Forensic Scientist*'', ''Medical Anthropologist.'', ''Museum Technician.'']' - The wingspan flies , the moth comes depending on the location from July to August . - source_sentence: Identify different forms of energy (e.g., light, sound, heat). sentences: - '`` Irreplaceable '''' '''' remained on the chart for thirty weeks , and was certified double-platinum by the Recording Industry Association of America ( RIAA ) , denoting sales of two million downloads , and had sold over 3,139,000 paid digital downloads in the US as of October 2012 , according to Nielsen SoundScan . ''''' - On Rotten Tomatoes , the film has a rating of 63 % , based on 87 reviews , with an average rating of 5.9/10 . - Heat, light, and sound are all different forms of energy. - source_sentence: what is so small it can only be seen with an electron microscope? sentences: - "Viruses are so small that they can be seen only with an electron microscope..\ \ Where most viruses are DNA, HIV is an RNA virus. \n HIV is so small it can only\ \ be seen with an electron microscope" - The development of modern lasers has opened many doors to both research and applications. A laser beam was used to measure the distance from the Earth to the moon. Lasers are important components of CD players. As the image above illustrates, lasers can provide precise focusing of beams to selectively destroy cancer cells in patients. The ability of a laser to focus precisely is due to high-quality crystals that help give rise to the laser beam. A variety of techniques are used to manufacture pure crystals for use in lasers. - Discussion for (a) This value is the net work done on the package. The person actually does more work than this, because friction opposes the motion. Friction does negative work and removes some of the energy the person expends and converts it to thermal energy. The net work equals the sum of the work done by each individual force. Strategy and Concept for (b) The forces acting on the package are gravity, the normal force, the force of friction, and the applied force. The normal force and force of gravity are each perpendicular to the displacement, and therefore do no work. Solution for (b) The applied force does work. - source_sentence: what aspects of your environment may relate to the epidemic of obesity sentences: - Jan Kromkamp ( born August 17 , 1980 in Makkinga , Netherlands ) is a Dutch footballer . - When chemicals in solution react, the proper way of writing the chemical formulas of the dissolved ionic compounds is in terms of the dissociated ions, not the complete ionic formula. A complete ionic equation is a chemical equation in which the dissolved ionic compounds are written as separated ions. Solubility rules are very useful in determining which ionic compounds are dissolved and which are not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation includes NaCl, AgNO3, and NaNO3 written as separated ions:. - Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the environment that promotes physical inactivity and intake of high-calorie foods suggests that genes do play a role in the development of obesity. model-index: - name: SentenceTransformer based on microsoft/deberta-v3-small results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.5639496482103532 name: Pearson Cosine - type: spearman_cosine value: 0.5729838735376087 name: Spearman Cosine - type: pearson_manhattan value: 0.5739911399908657 name: Pearson Manhattan - type: spearman_manhattan value: 0.5731718735867141 name: Spearman Manhattan - type: pearson_euclidean value: 0.5732787731326285 name: Pearson Euclidean - type: spearman_euclidean value: 0.5730641573005449 name: Spearman Euclidean - type: pearson_dot value: 0.5637779615119842 name: Pearson Dot - type: spearman_dot value: 0.5726842638425932 name: Spearman Dot - type: pearson_max value: 0.5739911399908657 name: Pearson Max - type: spearman_max value: 0.5731718735867141 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: allNLI dev type: allNLI-dev metrics: - type: cosine_accuracy value: 0.689453125 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.9270462989807129 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.5446808510638298 name: Cosine F1 - type: cosine_f1_threshold value: 0.8589708805084229 name: Cosine F1 Threshold - type: cosine_precision value: 0.43097643097643096 name: Cosine Precision - type: cosine_recall value: 0.7398843930635838 name: Cosine Recall - type: cosine_ap value: 0.5065548770111167 name: Cosine Ap - type: dot_accuracy value: 0.689453125 name: Dot Accuracy - type: dot_accuracy_threshold value: 712.409912109375 name: Dot Accuracy Threshold - type: dot_f1 value: 0.5446808510638298 name: Dot F1 - type: dot_f1_threshold value: 660.105224609375 name: Dot F1 Threshold - type: dot_precision value: 0.43097643097643096 name: Dot Precision - type: dot_recall value: 0.7398843930635838 name: Dot Recall - type: dot_ap value: 0.5065544970851109 name: Dot Ap - type: manhattan_accuracy value: 0.693359375 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 261.6011962890625 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.5442764578833693 name: Manhattan F1 - type: manhattan_f1_threshold value: 316.1423645019531 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.43448275862068964 name: Manhattan Precision - type: manhattan_recall value: 0.7283236994219653 name: Manhattan Recall - type: manhattan_ap value: 0.5104540022958912 name: Manhattan Ap - type: euclidean_accuracy value: 0.689453125 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 10.588932037353516 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.5446808510638298 name: Euclidean F1 - type: euclidean_f1_threshold value: 14.722663879394531 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.43097643097643096 name: Euclidean Precision - type: euclidean_recall value: 0.7398843930635838 name: Euclidean Recall - type: euclidean_ap value: 0.5065548770111167 name: Euclidean Ap - type: max_accuracy value: 0.693359375 name: Max Accuracy - type: max_accuracy_threshold value: 712.409912109375 name: Max Accuracy Threshold - type: max_f1 value: 0.5446808510638298 name: Max F1 - type: max_f1_threshold value: 660.105224609375 name: Max F1 Threshold - type: max_precision value: 0.43448275862068964 name: Max Precision - type: max_recall value: 0.7398843930635838 name: Max Recall - type: max_ap value: 0.5104540022958912 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Qnli dev type: Qnli-dev metrics: - type: cosine_accuracy value: 0.67578125 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8426380157470703 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6866197183098591 name: Cosine F1 - type: cosine_f1_threshold value: 0.8136929869651794 name: Cosine F1 Threshold - type: cosine_precision value: 0.5873493975903614 name: Cosine Precision - type: cosine_recall value: 0.826271186440678 name: Cosine Recall - type: cosine_ap value: 0.6951783685178275 name: Cosine Ap - type: dot_accuracy value: 0.67578125 name: Dot Accuracy - type: dot_accuracy_threshold value: 647.48828125 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6866197183098591 name: Dot F1 - type: dot_f1_threshold value: 625.2723388671875 name: Dot F1 Threshold - type: dot_precision value: 0.5873493975903614 name: Dot Precision - type: dot_recall value: 0.826271186440678 name: Dot Recall - type: dot_ap value: 0.6953081100663845 name: Dot Ap - type: manhattan_accuracy value: 0.673828125 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 337.1568603515625 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6913996627318718 name: Manhattan F1 - type: manhattan_f1_threshold value: 378.724609375 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5742296918767507 name: Manhattan Precision - type: manhattan_recall value: 0.8686440677966102 name: Manhattan Recall - type: manhattan_ap value: 0.6981557028078741 name: Manhattan Ap - type: euclidean_accuracy value: 0.67578125 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 15.551056861877441 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6866197183098591 name: Euclidean F1 - type: euclidean_f1_threshold value: 16.921308517456055 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5873493975903614 name: Euclidean Precision - type: euclidean_recall value: 0.826271186440678 name: Euclidean Recall - type: euclidean_ap value: 0.695195292717627 name: Euclidean Ap - type: max_accuracy value: 0.67578125 name: Max Accuracy - type: max_accuracy_threshold value: 647.48828125 name: Max Accuracy Threshold - type: max_f1 value: 0.6913996627318718 name: Max F1 - type: max_f1_threshold value: 625.2723388671875 name: Max F1 Threshold - type: max_precision value: 0.5873493975903614 name: Max Precision - type: max_recall value: 0.8686440677966102 name: Max Recall - type: max_ap value: 0.6981557028078741 name: Max Ap --- # SentenceTransformer based on microsoft/deberta-v3-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): AdvancedWeightedPooling( (alpha_dropout_layer): Dropout(p=0.05, inplace=False) (gate_dropout_layer): Dropout(p=0.0, inplace=False) (linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True) (linear_attnOut): Linear(in_features=768, out_features=768, bias=True) (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) ) (layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=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("bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp") # Run inference sentences = [ 'what aspects of your environment may relate to the epidemic of obesity', 'Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the environment that promotes physical inactivity and intake of high-calorie foods suggests that genes do play a role in the development of obesity.', 'When chemicals in solution react, the proper way of writing the chemical formulas of the dissolved ionic compounds is in terms of the dissociated ions, not the complete ionic formula. A complete ionic equation is a chemical equation in which the dissolved ionic compounds are written as separated ions. Solubility rules are very useful in determining which ionic compounds are dissolved and which are not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation includes NaCl, AgNO3, and NaNO3 written as separated ions:.', ] 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 * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:----------| | pearson_cosine | 0.5639 | | **spearman_cosine** | **0.573** | | pearson_manhattan | 0.574 | | spearman_manhattan | 0.5732 | | pearson_euclidean | 0.5733 | | spearman_euclidean | 0.5731 | | pearson_dot | 0.5638 | | spearman_dot | 0.5727 | | pearson_max | 0.574 | | spearman_max | 0.5732 | #### Binary Classification * Dataset: `allNLI-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6895 | | cosine_accuracy_threshold | 0.927 | | cosine_f1 | 0.5447 | | cosine_f1_threshold | 0.859 | | cosine_precision | 0.431 | | cosine_recall | 0.7399 | | cosine_ap | 0.5066 | | dot_accuracy | 0.6895 | | dot_accuracy_threshold | 712.4099 | | dot_f1 | 0.5447 | | dot_f1_threshold | 660.1052 | | dot_precision | 0.431 | | dot_recall | 0.7399 | | dot_ap | 0.5066 | | manhattan_accuracy | 0.6934 | | manhattan_accuracy_threshold | 261.6012 | | manhattan_f1 | 0.5443 | | manhattan_f1_threshold | 316.1424 | | manhattan_precision | 0.4345 | | manhattan_recall | 0.7283 | | manhattan_ap | 0.5105 | | euclidean_accuracy | 0.6895 | | euclidean_accuracy_threshold | 10.5889 | | euclidean_f1 | 0.5447 | | euclidean_f1_threshold | 14.7227 | | euclidean_precision | 0.431 | | euclidean_recall | 0.7399 | | euclidean_ap | 0.5066 | | max_accuracy | 0.6934 | | max_accuracy_threshold | 712.4099 | | max_f1 | 0.5447 | | max_f1_threshold | 660.1052 | | max_precision | 0.4345 | | max_recall | 0.7399 | | **max_ap** | **0.5105** | #### Binary Classification * Dataset: `Qnli-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6758 | | cosine_accuracy_threshold | 0.8426 | | cosine_f1 | 0.6866 | | cosine_f1_threshold | 0.8137 | | cosine_precision | 0.5873 | | cosine_recall | 0.8263 | | cosine_ap | 0.6952 | | dot_accuracy | 0.6758 | | dot_accuracy_threshold | 647.4883 | | dot_f1 | 0.6866 | | dot_f1_threshold | 625.2723 | | dot_precision | 0.5873 | | dot_recall | 0.8263 | | dot_ap | 0.6953 | | manhattan_accuracy | 0.6738 | | manhattan_accuracy_threshold | 337.1569 | | manhattan_f1 | 0.6914 | | manhattan_f1_threshold | 378.7246 | | manhattan_precision | 0.5742 | | manhattan_recall | 0.8686 | | manhattan_ap | 0.6982 | | euclidean_accuracy | 0.6758 | | euclidean_accuracy_threshold | 15.5511 | | euclidean_f1 | 0.6866 | | euclidean_f1_threshold | 16.9213 | | euclidean_precision | 0.5873 | | euclidean_recall | 0.8263 | | euclidean_ap | 0.6952 | | max_accuracy | 0.6758 | | max_accuracy_threshold | 647.4883 | | max_f1 | 0.6914 | | max_f1_threshold | 625.2723 | | max_precision | 0.5873 | | max_recall | 0.8686 | | **max_ap** | **0.6982** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 32,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | In which London road is Harrod’s department store? | Harrods, Brompton Road, London | Shopping/Department Stores in London | LondonTown.com Opening Times Britain's most famous store and possibly the most famous store in the world, Harrods features on many tourist 'must-see' lists - and with good reason. Its humble beginnings date back to 1849, when Charles Henry Harrod opened a small East End grocer and tea merchant business that emphasised impeccable service over value. Today, it occupies a vast seven floor site in London's fashionable Knightsbridge and boasts a phenomenal range of products from pianos and cooking pans to fashion and perfumery. The luxurious Urban Retreat can be found on the sixth floor while newer departments include Superbrands, with 17 boutiques from top international designers, and Salon du Parfums, housing some of the most exceptional and exclusive perfumes in the world. The Food Hall is ostentatious to the core and mouth-wateringly exotic, and the store as a whole is well served with 27 restaurants. At Christmas time the Brompton Road windows are transformed into a magical winter wonderland and Father Christmas takes up residence at the enchanting Christmas Grotto. The summer and winter sales are calendar events in the shopping year, and although both sales are extremely crowded there are some great bargains on offer. � | | e. in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently. | Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas | | Joe Cole was unable to join West Bromwich Albion . | On 16th October Joe Cole took a long hard look at himself realising that he would never get the opportunity to join West Bromwich Albion and joined Coventry City instead. | * Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.025} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 256 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06} - `warmup_ratio`: 0.33 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `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`: 256 - `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`: 3 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06} - `warmup_ratio`: 0.33 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `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`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `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 - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap | |:------:|:----:|:-------------:|:------------------------:|:-----------------:|:---------------:| | 0.0010 | 1 | 6.0688 | - | - | - | | 0.0020 | 2 | 7.5576 | - | - | - | | 0.0030 | 3 | 4.6849 | - | - | - | | 0.0039 | 4 | 5.4503 | - | - | - | | 0.0049 | 5 | 5.6057 | - | - | - | | 0.0059 | 6 | 6.3049 | - | - | - | | 0.0069 | 7 | 6.8336 | - | - | - | | 0.0079 | 8 | 5.0777 | - | - | - | | 0.0089 | 9 | 4.8358 | - | - | - | | 0.0098 | 10 | 4.641 | - | - | - | | 0.0108 | 11 | 4.828 | - | - | - | | 0.0118 | 12 | 5.2269 | - | - | - | | 0.0128 | 13 | 5.6772 | - | - | - | | 0.0138 | 14 | 5.1422 | - | - | - | | 0.0148 | 15 | 6.2469 | - | - | - | | 0.0157 | 16 | 4.6802 | - | - | - | | 0.0167 | 17 | 4.5492 | - | - | - | | 0.0177 | 18 | 4.8062 | - | - | - | | 0.0187 | 19 | 7.5141 | - | - | - | | 0.0197 | 20 | 5.5202 | - | - | - | | 0.0207 | 21 | 6.5025 | - | - | - | | 0.0217 | 22 | 7.318 | - | - | - | | 0.0226 | 23 | 4.6458 | - | - | - | | 0.0236 | 24 | 4.6191 | - | - | - | | 0.0246 | 25 | 4.3159 | - | - | - | | 0.0256 | 26 | 6.3677 | - | - | - | | 0.0266 | 27 | 5.6052 | - | - | - | | 0.0276 | 28 | 4.196 | - | - | - | | 0.0285 | 29 | 4.4802 | - | - | - | | 0.0295 | 30 | 4.9193 | - | - | - | | 0.0305 | 31 | 4.0996 | - | - | - | | 0.0315 | 32 | 5.6307 | - | - | - | | 0.0325 | 33 | 4.5745 | - | - | - | | 0.0335 | 34 | 4.4514 | - | - | - | | 0.0344 | 35 | 4.0617 | - | - | - | | 0.0354 | 36 | 5.0298 | - | - | - | | 0.0364 | 37 | 3.9815 | - | - | - | | 0.0374 | 38 | 4.0871 | - | - | - | | 0.0384 | 39 | 4.2378 | - | - | - | | 0.0394 | 40 | 3.8226 | - | - | - | | 0.0404 | 41 | 4.3519 | - | - | - | | 0.0413 | 42 | 3.6345 | - | - | - | | 0.0423 | 43 | 5.0829 | - | - | - | | 0.0433 | 44 | 4.6701 | - | - | - | | 0.0443 | 45 | 4.1371 | - | - | - | | 0.0453 | 46 | 4.2418 | - | - | - | | 0.0463 | 47 | 4.4766 | - | - | - | | 0.0472 | 48 | 4.4797 | - | - | - | | 0.0482 | 49 | 3.8471 | - | - | - | | 0.0492 | 50 | 4.3194 | - | - | - | | 0.0502 | 51 | 3.9426 | - | - | - | | 0.0512 | 52 | 3.5333 | - | - | - | | 0.0522 | 53 | 4.2426 | - | - | - | | 0.0531 | 54 | 3.9816 | - | - | - | | 0.0541 | 55 | 3.663 | - | - | - | | 0.0551 | 56 | 3.9057 | - | - | - | | 0.0561 | 57 | 4.0345 | - | - | - | | 0.0571 | 58 | 3.5233 | - | - | - | | 0.0581 | 59 | 3.7999 | - | - | - | | 0.0591 | 60 | 3.1885 | - | - | - | | 0.0600 | 61 | 3.6013 | - | - | - | | 0.0610 | 62 | 3.392 | - | - | - | | 0.0620 | 63 | 3.3814 | - | - | - | | 0.0630 | 64 | 4.0428 | - | - | - | | 0.0640 | 65 | 3.7825 | - | - | - | | 0.0650 | 66 | 3.4181 | - | - | - | | 0.0659 | 67 | 3.7793 | - | - | - | | 0.0669 | 68 | 3.8344 | - | - | - | | 0.0679 | 69 | 3.2165 | - | - | - | | 0.0689 | 70 | 3.3811 | - | - | - | | 0.0699 | 71 | 3.5984 | - | - | - | | 0.0709 | 72 | 3.8583 | - | - | - | | 0.0719 | 73 | 3.296 | - | - | - | | 0.0728 | 74 | 2.7661 | - | - | - | | 0.0738 | 75 | 2.9805 | - | - | - | | 0.0748 | 76 | 2.566 | - | - | - | | 0.0758 | 77 | 3.258 | - | - | - | | 0.0768 | 78 | 3.3804 | - | - | - | | 0.0778 | 79 | 2.8828 | - | - | - | | 0.0787 | 80 | 3.1077 | - | - | - | | 0.0797 | 81 | 2.9441 | - | - | - | | 0.0807 | 82 | 2.9465 | - | - | - | | 0.0817 | 83 | 2.7088 | - | - | - | | 0.0827 | 84 | 2.9215 | - | - | - | | 0.0837 | 85 | 3.4698 | - | - | - | | 0.0846 | 86 | 2.2414 | - | - | - | | 0.0856 | 87 | 3.1601 | - | - | - | | 0.0866 | 88 | 2.7714 | - | - | - | | 0.0876 | 89 | 3.0311 | - | - | - | | 0.0886 | 90 | 3.0336 | - | - | - | | 0.0896 | 91 | 1.9358 | - | - | - | | 0.0906 | 92 | 2.6031 | - | - | - | | 0.0915 | 93 | 2.7515 | - | - | - | | 0.0925 | 94 | 2.8496 | - | - | - | | 0.0935 | 95 | 1.8015 | - | - | - | | 0.0945 | 96 | 2.8138 | - | - | - | | 0.0955 | 97 | 2.0597 | - | - | - | | 0.0965 | 98 | 2.1053 | - | - | - | | 0.0974 | 99 | 2.6785 | - | - | - | | 0.0984 | 100 | 2.588 | - | - | - | | 0.0994 | 101 | 2.0099 | - | - | - | | 0.1004 | 102 | 2.7947 | - | - | - | | 0.1014 | 103 | 2.3274 | - | - | - | | 0.1024 | 104 | 2.2545 | - | - | - | | 0.1033 | 105 | 2.4575 | - | - | - | | 0.1043 | 106 | 2.4413 | - | - | - | | 0.1053 | 107 | 2.3185 | - | - | - | | 0.1063 | 108 | 2.1577 | - | - | - | | 0.1073 | 109 | 2.1278 | - | - | - | | 0.1083 | 110 | 2.0967 | - | - | - | | 0.1093 | 111 | 2.6142 | - | - | - | | 0.1102 | 112 | 1.8553 | - | - | - | | 0.1112 | 113 | 2.1523 | - | - | - | | 0.1122 | 114 | 2.1726 | - | - | - | | 0.1132 | 115 | 1.8564 | - | - | - | | 0.1142 | 116 | 1.8413 | - | - | - | | 0.1152 | 117 | 2.0441 | - | - | - | | 0.1161 | 118 | 2.2159 | - | - | - | | 0.1171 | 119 | 2.6779 | - | - | - | | 0.1181 | 120 | 2.2976 | - | - | - | | 0.1191 | 121 | 1.9407 | - | - | - | | 0.1201 | 122 | 1.9019 | - | - | - | | 0.1211 | 123 | 2.2149 | - | - | - | | 0.1220 | 124 | 1.6823 | - | - | - | | 0.1230 | 125 | 1.8402 | - | - | - | | 0.1240 | 126 | 1.6914 | - | - | - | | 0.125 | 127 | 2.1626 | - | - | - | | 0.1260 | 128 | 1.6414 | - | - | - | | 0.1270 | 129 | 2.2043 | - | - | - | | 0.1280 | 130 | 1.9987 | - | - | - | | 0.1289 | 131 | 1.8868 | - | - | - | | 0.1299 | 132 | 1.8262 | - | - | - | | 0.1309 | 133 | 2.0404 | - | - | - | | 0.1319 | 134 | 1.9134 | - | - | - | | 0.1329 | 135 | 2.3725 | - | - | - | | 0.1339 | 136 | 1.4127 | - | - | - | | 0.1348 | 137 | 1.6876 | - | - | - | | 0.1358 | 138 | 1.8376 | - | - | - | | 0.1368 | 139 | 1.6992 | - | - | - | | 0.1378 | 140 | 1.5032 | - | - | - | | 0.1388 | 141 | 2.0334 | - | - | - | | 0.1398 | 142 | 2.3581 | - | - | - | | 0.1407 | 143 | 1.4236 | - | - | - | | 0.1417 | 144 | 2.202 | - | - | - | | 0.1427 | 145 | 1.7654 | - | - | - | | 0.1437 | 146 | 1.5748 | - | - | - | | 0.1447 | 147 | 1.7996 | - | - | - | | 0.1457 | 148 | 1.7517 | - | - | - | | 0.1467 | 149 | 1.8933 | - | - | - | | 0.1476 | 150 | 1.2836 | - | - | - | | 0.1486 | 151 | 1.7145 | - | - | - | | 0.1496 | 152 | 1.6499 | - | - | - | | 0.1506 | 153 | 1.8273 | 0.4057 | 0.4389 | 0.6725 | | 0.1516 | 154 | 2.2859 | - | - | - | | 0.1526 | 155 | 1.0833 | - | - | - | | 0.1535 | 156 | 1.6829 | - | - | - | | 0.1545 | 157 | 2.1464 | - | - | - | | 0.1555 | 158 | 1.745 | - | - | - | | 0.1565 | 159 | 1.7319 | - | - | - | | 0.1575 | 160 | 1.6968 | - | - | - | | 0.1585 | 161 | 1.7401 | - | - | - | | 0.1594 | 162 | 1.729 | - | - | - | | 0.1604 | 163 | 2.0782 | - | - | - | | 0.1614 | 164 | 2.6545 | - | - | - | | 0.1624 | 165 | 1.4045 | - | - | - | | 0.1634 | 166 | 1.2937 | - | - | - | | 0.1644 | 167 | 1.1171 | - | - | - | | 0.1654 | 168 | 1.3537 | - | - | - | | 0.1663 | 169 | 1.7028 | - | - | - | | 0.1673 | 170 | 1.4143 | - | - | - | | 0.1683 | 171 | 1.8648 | - | - | - | | 0.1693 | 172 | 1.6768 | - | - | - | | 0.1703 | 173 | 1.9528 | - | - | - | | 0.1713 | 174 | 1.1718 | - | - | - | | 0.1722 | 175 | 1.8176 | - | - | - | | 0.1732 | 176 | 0.8439 | - | - | - | | 0.1742 | 177 | 1.5092 | - | - | - | | 0.1752 | 178 | 1.1947 | - | - | - | | 0.1762 | 179 | 1.6395 | - | - | - | | 0.1772 | 180 | 1.4394 | - | - | - | | 0.1781 | 181 | 1.7548 | - | - | - | | 0.1791 | 182 | 1.1181 | - | - | - | | 0.1801 | 183 | 1.0271 | - | - | - | | 0.1811 | 184 | 2.3108 | - | - | - | | 0.1821 | 185 | 2.1242 | - | - | - | | 0.1831 | 186 | 1.9822 | - | - | - | | 0.1841 | 187 | 2.3605 | - | - | - | | 0.1850 | 188 | 1.5251 | - | - | - | | 0.1860 | 189 | 1.2351 | - | - | - | | 0.1870 | 190 | 1.5859 | - | - | - | | 0.1880 | 191 | 1.8056 | - | - | - | | 0.1890 | 192 | 1.349 | - | - | - | | 0.1900 | 193 | 0.893 | - | - | - | | 0.1909 | 194 | 1.5122 | - | - | - | | 0.1919 | 195 | 1.3875 | - | - | - | | 0.1929 | 196 | 1.29 | - | - | - | | 0.1939 | 197 | 2.2931 | - | - | - | | 0.1949 | 198 | 1.2663 | - | - | - | | 0.1959 | 199 | 1.9712 | - | - | - | | 0.1969 | 200 | 2.3307 | - | - | - | | 0.1978 | 201 | 1.6544 | - | - | - | | 0.1988 | 202 | 1.638 | - | - | - | | 0.1998 | 203 | 1.3412 | - | - | - | | 0.2008 | 204 | 1.4454 | - | - | - | | 0.2018 | 205 | 1.5437 | - | - | - | | 0.2028 | 206 | 1.4921 | - | - | - | | 0.2037 | 207 | 1.4298 | - | - | - | | 0.2047 | 208 | 1.6174 | - | - | - | | 0.2057 | 209 | 1.4137 | - | - | - | | 0.2067 | 210 | 1.5652 | - | - | - | | 0.2077 | 211 | 1.1631 | - | - | - | | 0.2087 | 212 | 1.2351 | - | - | - | | 0.2096 | 213 | 1.7537 | - | - | - | | 0.2106 | 214 | 1.3186 | - | - | - | | 0.2116 | 215 | 1.2258 | - | - | - | | 0.2126 | 216 | 0.7695 | - | - | - | | 0.2136 | 217 | 1.2775 | - | - | - | | 0.2146 | 218 | 1.6795 | - | - | - | | 0.2156 | 219 | 1.2862 | - | - | - | | 0.2165 | 220 | 1.1723 | - | - | - | | 0.2175 | 221 | 1.3322 | - | - | - | | 0.2185 | 222 | 1.7564 | - | - | - | | 0.2195 | 223 | 1.1071 | - | - | - | | 0.2205 | 224 | 1.2011 | - | - | - | | 0.2215 | 225 | 1.2303 | - | - | - | | 0.2224 | 226 | 1.212 | - | - | - | | 0.2234 | 227 | 1.0117 | - | - | - | | 0.2244 | 228 | 1.1907 | - | - | - | | 0.2254 | 229 | 2.1293 | - | - | - | | 0.2264 | 230 | 1.3063 | - | - | - | | 0.2274 | 231 | 1.2841 | - | - | - | | 0.2283 | 232 | 1.3778 | - | - | - | | 0.2293 | 233 | 1.2242 | - | - | - | | 0.2303 | 234 | 0.9227 | - | - | - | | 0.2313 | 235 | 1.2221 | - | - | - | | 0.2323 | 236 | 2.1041 | - | - | - | | 0.2333 | 237 | 1.3341 | - | - | - | | 0.2343 | 238 | 1.0876 | - | - | - | | 0.2352 | 239 | 1.3328 | - | - | - | | 0.2362 | 240 | 1.2958 | - | - | - | | 0.2372 | 241 | 1.1522 | - | - | - | | 0.2382 | 242 | 1.7942 | - | - | - | | 0.2392 | 243 | 1.1325 | - | - | - | | 0.2402 | 244 | 1.6466 | - | - | - | | 0.2411 | 245 | 1.4608 | - | - | - | | 0.2421 | 246 | 0.6375 | - | - | - | | 0.2431 | 247 | 2.0177 | - | - | - | | 0.2441 | 248 | 1.2069 | - | - | - | | 0.2451 | 249 | 0.7639 | - | - | - | | 0.2461 | 250 | 1.3465 | - | - | - | | 0.2470 | 251 | 1.064 | - | - | - | | 0.2480 | 252 | 1.3757 | - | - | - | | 0.2490 | 253 | 1.612 | - | - | - | | 0.25 | 254 | 0.7917 | - | - | - | | 0.2510 | 255 | 1.5515 | - | - | - | | 0.2520 | 256 | 0.799 | - | - | - | | 0.2530 | 257 | 0.9882 | - | - | - | | 0.2539 | 258 | 1.1814 | - | - | - | | 0.2549 | 259 | 0.6394 | - | - | - | | 0.2559 | 260 | 1.4756 | - | - | - | | 0.2569 | 261 | 0.5338 | - | - | - | | 0.2579 | 262 | 0.9779 | - | - | - | | 0.2589 | 263 | 1.5307 | - | - | - | | 0.2598 | 264 | 1.1213 | - | - | - | | 0.2608 | 265 | 0.9482 | - | - | - | | 0.2618 | 266 | 0.9599 | - | - | - | | 0.2628 | 267 | 1.4455 | - | - | - | | 0.2638 | 268 | 1.6496 | - | - | - | | 0.2648 | 269 | 0.7402 | - | - | - | | 0.2657 | 270 | 0.7835 | - | - | - | | 0.2667 | 271 | 0.7821 | - | - | - | | 0.2677 | 272 | 1.5422 | - | - | - | | 0.2687 | 273 | 1.0995 | - | - | - | | 0.2697 | 274 | 1.378 | - | - | - | | 0.2707 | 275 | 1.3562 | - | - | - | | 0.2717 | 276 | 0.7376 | - | - | - | | 0.2726 | 277 | 1.1678 | - | - | - | | 0.2736 | 278 | 1.2989 | - | - | - | | 0.2746 | 279 | 1.9559 | - | - | - | | 0.2756 | 280 | 1.1237 | - | - | - | | 0.2766 | 281 | 0.952 | - | - | - | | 0.2776 | 282 | 1.6629 | - | - | - | | 0.2785 | 283 | 1.871 | - | - | - | | 0.2795 | 284 | 1.5946 | - | - | - | | 0.2805 | 285 | 1.4456 | - | - | - | | 0.2815 | 286 | 1.4085 | - | - | - | | 0.2825 | 287 | 1.1394 | - | - | - | | 0.2835 | 288 | 1.0315 | - | - | - | | 0.2844 | 289 | 1.488 | - | - | - | | 0.2854 | 290 | 1.4006 | - | - | - | | 0.2864 | 291 | 0.9237 | - | - | - | | 0.2874 | 292 | 1.163 | - | - | - | | 0.2884 | 293 | 1.7037 | - | - | - | | 0.2894 | 294 | 0.8715 | - | - | - | | 0.2904 | 295 | 1.2101 | - | - | - | | 0.2913 | 296 | 1.1179 | - | - | - | | 0.2923 | 297 | 1.3986 | - | - | - | | 0.2933 | 298 | 1.7068 | - | - | - | | 0.2943 | 299 | 0.8695 | - | - | - | | 0.2953 | 300 | 1.3778 | - | - | - | | 0.2963 | 301 | 1.2834 | - | - | - | | 0.2972 | 302 | 0.8123 | - | - | - | | 0.2982 | 303 | 1.6521 | - | - | - | | 0.2992 | 304 | 1.1064 | - | - | - | | 0.3002 | 305 | 0.9578 | - | - | - | | 0.3012 | 306 | 0.9254 | 0.4888 | 0.4789 | 0.7040 | | 0.3022 | 307 | 0.7541 | - | - | - | | 0.3031 | 308 | 0.7324 | - | - | - | | 0.3041 | 309 | 0.5974 | - | - | - | | 0.3051 | 310 | 1.1481 | - | - | - | | 0.3061 | 311 | 1.6179 | - | - | - | | 0.3071 | 312 | 1.4641 | - | - | - | | 0.3081 | 313 | 1.7185 | - | - | - | | 0.3091 | 314 | 0.9328 | - | - | - | | 0.3100 | 315 | 0.742 | - | - | - | | 0.3110 | 316 | 1.4173 | - | - | - | | 0.3120 | 317 | 0.7267 | - | - | - | | 0.3130 | 318 | 0.9494 | - | - | - | | 0.3140 | 319 | 1.5111 | - | - | - | | 0.3150 | 320 | 1.6949 | - | - | - | | 0.3159 | 321 | 1.7562 | - | - | - | | 0.3169 | 322 | 1.2532 | - | - | - | | 0.3179 | 323 | 1.1086 | - | - | - | | 0.3189 | 324 | 0.7377 | - | - | - | | 0.3199 | 325 | 1.085 | - | - | - | | 0.3209 | 326 | 0.7767 | - | - | - | | 0.3219 | 327 | 1.4441 | - | - | - | | 0.3228 | 328 | 0.8146 | - | - | - | | 0.3238 | 329 | 0.7403 | - | - | - | | 0.3248 | 330 | 0.8476 | - | - | - | | 0.3258 | 331 | 0.7323 | - | - | - | | 0.3268 | 332 | 1.2241 | - | - | - | | 0.3278 | 333 | 1.5065 | - | - | - | | 0.3287 | 334 | 0.5259 | - | - | - | | 0.3297 | 335 | 1.3103 | - | - | - | | 0.3307 | 336 | 0.8655 | - | - | - | | 0.3317 | 337 | 0.7575 | - | - | - | | 0.3327 | 338 | 1.968 | - | - | - | | 0.3337 | 339 | 1.317 | - | - | - | | 0.3346 | 340 | 1.1972 | - | - | - | | 0.3356 | 341 | 1.6323 | - | - | - | | 0.3366 | 342 | 1.0469 | - | - | - | | 0.3376 | 343 | 1.3349 | - | - | - | | 0.3386 | 344 | 0.9544 | - | - | - | | 0.3396 | 345 | 1.1894 | - | - | - | | 0.3406 | 346 | 0.7717 | - | - | - | | 0.3415 | 347 | 1.2563 | - | - | - | | 0.3425 | 348 | 1.2437 | - | - | - | | 0.3435 | 349 | 0.7806 | - | - | - | | 0.3445 | 350 | 0.8303 | - | - | - | | 0.3455 | 351 | 1.0926 | - | - | - | | 0.3465 | 352 | 0.6654 | - | - | - | | 0.3474 | 353 | 1.1087 | - | - | - | | 0.3484 | 354 | 1.1525 | - | - | - | | 0.3494 | 355 | 1.1127 | - | - | - | | 0.3504 | 356 | 1.4267 | - | - | - | | 0.3514 | 357 | 0.6148 | - | - | - | | 0.3524 | 358 | 1.0123 | - | - | - | | 0.3533 | 359 | 1.9682 | - | - | - | | 0.3543 | 360 | 0.8487 | - | - | - | | 0.3553 | 361 | 1.0412 | - | - | - | | 0.3563 | 362 | 1.0902 | - | - | - | | 0.3573 | 363 | 0.9606 | - | - | - | | 0.3583 | 364 | 0.9206 | - | - | - | | 0.3593 | 365 | 1.4727 | - | - | - | | 0.3602 | 366 | 0.9379 | - | - | - | | 0.3612 | 367 | 0.8387 | - | - | - | | 0.3622 | 368 | 0.9692 | - | - | - | | 0.3632 | 369 | 1.6298 | - | - | - | | 0.3642 | 370 | 1.0882 | - | - | - | | 0.3652 | 371 | 1.1558 | - | - | - | | 0.3661 | 372 | 0.9546 | - | - | - | | 0.3671 | 373 | 1.0124 | - | - | - | | 0.3681 | 374 | 1.3916 | - | - | - | | 0.3691 | 375 | 0.527 | - | - | - | | 0.3701 | 376 | 0.6387 | - | - | - | | 0.3711 | 377 | 1.1445 | - | - | - | | 0.3720 | 378 | 1.3309 | - | - | - | | 0.3730 | 379 | 1.5888 | - | - | - | | 0.3740 | 380 | 1.4422 | - | - | - | | 0.375 | 381 | 1.7044 | - | - | - | | 0.3760 | 382 | 0.7913 | - | - | - | | 0.3770 | 383 | 1.3241 | - | - | - | | 0.3780 | 384 | 0.6473 | - | - | - | | 0.3789 | 385 | 1.221 | - | - | - | | 0.3799 | 386 | 0.7773 | - | - | - | | 0.3809 | 387 | 1.054 | - | - | - | | 0.3819 | 388 | 0.9862 | - | - | - | | 0.3829 | 389 | 0.9684 | - | - | - | | 0.3839 | 390 | 1.3244 | - | - | - | | 0.3848 | 391 | 1.1787 | - | - | - | | 0.3858 | 392 | 1.4698 | - | - | - | | 0.3868 | 393 | 1.0961 | - | - | - | | 0.3878 | 394 | 1.1364 | - | - | - | | 0.3888 | 395 | 0.9368 | - | - | - | | 0.3898 | 396 | 1.1731 | - | - | - | | 0.3907 | 397 | 0.8686 | - | - | - | | 0.3917 | 398 | 0.7481 | - | - | - | | 0.3927 | 399 | 0.7261 | - | - | - | | 0.3937 | 400 | 1.2062 | - | - | - | | 0.3947 | 401 | 0.7462 | - | - | - | | 0.3957 | 402 | 1.0318 | - | - | - | | 0.3967 | 403 | 1.105 | - | - | - | | 0.3976 | 404 | 1.009 | - | - | - | | 0.3986 | 405 | 0.5941 | - | - | - | | 0.3996 | 406 | 1.7972 | - | - | - | | 0.4006 | 407 | 1.0544 | - | - | - | | 0.4016 | 408 | 1.3912 | - | - | - | | 0.4026 | 409 | 0.8305 | - | - | - | | 0.4035 | 410 | 0.8688 | - | - | - | | 0.4045 | 411 | 1.0069 | - | - | - | | 0.4055 | 412 | 1.3141 | - | - | - | | 0.4065 | 413 | 1.1042 | - | - | - | | 0.4075 | 414 | 1.1011 | - | - | - | | 0.4085 | 415 | 1.1192 | - | - | - | | 0.4094 | 416 | 1.5957 | - | - | - | | 0.4104 | 417 | 1.164 | - | - | - | | 0.4114 | 418 | 0.6425 | - | - | - | | 0.4124 | 419 | 0.6068 | - | - | - | | 0.4134 | 420 | 0.9275 | - | - | - | | 0.4144 | 421 | 0.8836 | - | - | - | | 0.4154 | 422 | 1.2115 | - | - | - | | 0.4163 | 423 | 0.8367 | - | - | - | | 0.4173 | 424 | 1.0595 | - | - | - | | 0.4183 | 425 | 0.826 | - | - | - | | 0.4193 | 426 | 0.707 | - | - | - | | 0.4203 | 427 | 0.6235 | - | - | - | | 0.4213 | 428 | 0.7719 | - | - | - | | 0.4222 | 429 | 1.0862 | - | - | - | | 0.4232 | 430 | 0.9311 | - | - | - | | 0.4242 | 431 | 1.2339 | - | - | - | | 0.4252 | 432 | 0.9891 | - | - | - | | 0.4262 | 433 | 1.8443 | - | - | - | | 0.4272 | 434 | 1.1799 | - | - | - | | 0.4281 | 435 | 0.759 | - | - | - | | 0.4291 | 436 | 1.1002 | - | - | - | | 0.4301 | 437 | 0.9141 | - | - | - | | 0.4311 | 438 | 0.5467 | - | - | - | | 0.4321 | 439 | 0.7476 | - | - | - | | 0.4331 | 440 | 1.14 | - | - | - | | 0.4341 | 441 | 1.1504 | - | - | - | | 0.4350 | 442 | 1.26 | - | - | - | | 0.4360 | 443 | 1.0311 | - | - | - | | 0.4370 | 444 | 1.0646 | - | - | - | | 0.4380 | 445 | 0.8687 | - | - | - | | 0.4390 | 446 | 0.6839 | - | - | - | | 0.4400 | 447 | 1.1376 | - | - | - | | 0.4409 | 448 | 0.9759 | - | - | - | | 0.4419 | 449 | 0.7971 | - | - | - | | 0.4429 | 450 | 0.9708 | - | - | - | | 0.4439 | 451 | 0.8217 | - | - | - | | 0.4449 | 452 | 1.3728 | - | - | - | | 0.4459 | 453 | 0.9119 | - | - | - | | 0.4469 | 454 | 1.012 | - | - | - | | 0.4478 | 455 | 1.3738 | - | - | - | | 0.4488 | 456 | 0.8219 | - | - | - | | 0.4498 | 457 | 1.2558 | - | - | - | | 0.4508 | 458 | 0.6247 | - | - | - | | 0.4518 | 459 | 0.7295 | 0.5410 | 0.4920 | 0.6879 | | 0.4528 | 460 | 0.8154 | - | - | - | | 0.4537 | 461 | 1.1392 | - | - | - | | 0.4547 | 462 | 0.8618 | - | - | - | | 0.4557 | 463 | 0.9669 | - | - | - | | 0.4567 | 464 | 0.8804 | - | - | - | | 0.4577 | 465 | 0.8479 | - | - | - | | 0.4587 | 466 | 0.6296 | - | - | - | | 0.4596 | 467 | 0.8449 | - | - | - | | 0.4606 | 468 | 0.9772 | - | - | - | | 0.4616 | 469 | 0.6424 | - | - | - | | 0.4626 | 470 | 0.9169 | - | - | - | | 0.4636 | 471 | 0.7599 | - | - | - | | 0.4646 | 472 | 0.8943 | - | - | - | | 0.4656 | 473 | 0.9475 | - | - | - | | 0.4665 | 474 | 1.4518 | - | - | - | | 0.4675 | 475 | 1.274 | - | - | - | | 0.4685 | 476 | 0.7306 | - | - | - | | 0.4695 | 477 | 0.9238 | - | - | - | | 0.4705 | 478 | 0.6593 | - | - | - | | 0.4715 | 479 | 1.0183 | - | - | - | | 0.4724 | 480 | 1.2577 | - | - | - | | 0.4734 | 481 | 0.8738 | - | - | - | | 0.4744 | 482 | 1.1416 | - | - | - | | 0.4754 | 483 | 0.7135 | - | - | - | | 0.4764 | 484 | 1.2587 | - | - | - | | 0.4774 | 485 | 0.8823 | - | - | - | | 0.4783 | 486 | 0.8423 | - | - | - | | 0.4793 | 487 | 0.7704 | - | - | - | | 0.4803 | 488 | 0.7049 | - | - | - | | 0.4813 | 489 | 1.1893 | - | - | - | | 0.4823 | 490 | 1.3985 | - | - | - | | 0.4833 | 491 | 1.3567 | - | - | - | | 0.4843 | 492 | 1.2573 | - | - | - | | 0.4852 | 493 | 0.7671 | - | - | - | | 0.4862 | 494 | 0.5425 | - | - | - | | 0.4872 | 495 | 0.9372 | - | - | - | | 0.4882 | 496 | 0.799 | - | - | - | | 0.4892 | 497 | 0.9548 | - | - | - | | 0.4902 | 498 | 1.0855 | - | - | - | | 0.4911 | 499 | 1.0465 | - | - | - | | 0.4921 | 500 | 1.1004 | - | - | - | | 0.4931 | 501 | 0.6392 | - | - | - | | 0.4941 | 502 | 0.7102 | - | - | - | | 0.4951 | 503 | 1.3242 | - | - | - | | 0.4961 | 504 | 0.6861 | - | - | - | | 0.4970 | 505 | 0.9291 | - | - | - | | 0.4980 | 506 | 0.8592 | - | - | - | | 0.4990 | 507 | 0.9462 | - | - | - | | 0.5 | 508 | 1.0167 | - | - | - | | 0.5010 | 509 | 1.0118 | - | - | - | | 0.5020 | 510 | 0.6741 | - | - | - | | 0.5030 | 511 | 1.4578 | - | - | - | | 0.5039 | 512 | 1.2959 | - | - | - | | 0.5049 | 513 | 0.8533 | - | - | - | | 0.5059 | 514 | 0.6685 | - | - | - | | 0.5069 | 515 | 1.1556 | - | - | - | | 0.5079 | 516 | 0.8177 | - | - | - | | 0.5089 | 517 | 0.6296 | - | - | - | | 0.5098 | 518 | 0.8407 | - | - | - | | 0.5108 | 519 | 0.6987 | - | - | - | | 0.5118 | 520 | 0.9888 | - | - | - | | 0.5128 | 521 | 0.8938 | - | - | - | | 0.5138 | 522 | 0.582 | - | - | - | | 0.5148 | 523 | 0.6596 | - | - | - | | 0.5157 | 524 | 0.6029 | - | - | - | | 0.5167 | 525 | 0.9806 | - | - | - | | 0.5177 | 526 | 0.9463 | - | - | - | | 0.5187 | 527 | 0.7088 | - | - | - | | 0.5197 | 528 | 0.7525 | - | - | - | | 0.5207 | 529 | 0.7625 | - | - | - | | 0.5217 | 530 | 0.8271 | - | - | - | | 0.5226 | 531 | 0.6129 | - | - | - | | 0.5236 | 532 | 1.1563 | - | - | - | | 0.5246 | 533 | 0.8131 | - | - | - | | 0.5256 | 534 | 0.5363 | - | - | - | | 0.5266 | 535 | 0.8819 | - | - | - | | 0.5276 | 536 | 0.9772 | - | - | - | | 0.5285 | 537 | 1.2102 | - | - | - | | 0.5295 | 538 | 1.1234 | - | - | - | | 0.5305 | 539 | 1.1857 | - | - | - | | 0.5315 | 540 | 0.7873 | - | - | - | | 0.5325 | 541 | 0.5034 | - | - | - | | 0.5335 | 542 | 1.3305 | - | - | - | | 0.5344 | 543 | 1.1727 | - | - | - | | 0.5354 | 544 | 1.2825 | - | - | - | | 0.5364 | 545 | 1.0446 | - | - | - | | 0.5374 | 546 | 0.9838 | - | - | - | | 0.5384 | 547 | 1.2194 | - | - | - | | 0.5394 | 548 | 0.7709 | - | - | - | | 0.5404 | 549 | 0.748 | - | - | - | | 0.5413 | 550 | 1.0948 | - | - | - | | 0.5423 | 551 | 0.915 | - | - | - | | 0.5433 | 552 | 1.537 | - | - | - | | 0.5443 | 553 | 0.3239 | - | - | - | | 0.5453 | 554 | 0.9592 | - | - | - | | 0.5463 | 555 | 0.7737 | - | - | - | | 0.5472 | 556 | 0.613 | - | - | - | | 0.5482 | 557 | 1.3646 | - | - | - | | 0.5492 | 558 | 0.6659 | - | - | - | | 0.5502 | 559 | 0.5207 | - | - | - | | 0.5512 | 560 | 0.9467 | - | - | - | | 0.5522 | 561 | 0.5692 | - | - | - | | 0.5531 | 562 | 1.5855 | - | - | - | | 0.5541 | 563 | 0.8855 | - | - | - | | 0.5551 | 564 | 1.1829 | - | - | - | | 0.5561 | 565 | 0.978 | - | - | - | | 0.5571 | 566 | 1.1818 | - | - | - | | 0.5581 | 567 | 0.701 | - | - | - | | 0.5591 | 568 | 1.0226 | - | - | - | | 0.5600 | 569 | 0.5937 | - | - | - | | 0.5610 | 570 | 0.8095 | - | - | - | | 0.5620 | 571 | 1.174 | - | - | - | | 0.5630 | 572 | 0.96 | - | - | - | | 0.5640 | 573 | 0.8339 | - | - | - | | 0.5650 | 574 | 0.717 | - | - | - | | 0.5659 | 575 | 0.5938 | - | - | - | | 0.5669 | 576 | 0.6501 | - | - | - | | 0.5679 | 577 | 0.7003 | - | - | - | | 0.5689 | 578 | 0.5525 | - | - | - | | 0.5699 | 579 | 0.7003 | - | - | - | | 0.5709 | 580 | 1.059 | - | - | - | | 0.5719 | 581 | 0.8625 | - | - | - | | 0.5728 | 582 | 0.5862 | - | - | - | | 0.5738 | 583 | 0.9162 | - | - | - | | 0.5748 | 584 | 0.926 | - | - | - | | 0.5758 | 585 | 1.2729 | - | - | - | | 0.5768 | 586 | 0.8935 | - | - | - | | 0.5778 | 587 | 0.541 | - | - | - | | 0.5787 | 588 | 1.1455 | - | - | - | | 0.5797 | 589 | 0.7306 | - | - | - | | 0.5807 | 590 | 0.9088 | - | - | - | | 0.5817 | 591 | 0.9166 | - | - | - | | 0.5827 | 592 | 0.8679 | - | - | - | | 0.5837 | 593 | 0.9329 | - | - | - | | 0.5846 | 594 | 1.1201 | - | - | - | | 0.5856 | 595 | 0.6418 | - | - | - | | 0.5866 | 596 | 1.145 | - | - | - | | 0.5876 | 597 | 1.4041 | - | - | - | | 0.5886 | 598 | 0.6954 | - | - | - | | 0.5896 | 599 | 0.4567 | - | - | - | | 0.5906 | 600 | 1.1305 | - | - | - | | 0.5915 | 601 | 0.8077 | - | - | - | | 0.5925 | 602 | 0.6143 | - | - | - | | 0.5935 | 603 | 1.3139 | - | - | - | | 0.5945 | 604 | 0.7694 | - | - | - | | 0.5955 | 605 | 0.9622 | - | - | - | | 0.5965 | 606 | 0.91 | - | - | - | | 0.5974 | 607 | 1.3125 | - | - | - | | 0.5984 | 608 | 1.0153 | - | - | - | | 0.5994 | 609 | 0.8468 | - | - | - | | 0.6004 | 610 | 1.1026 | - | - | - | | 0.6014 | 611 | 0.8291 | - | - | - | | 0.6024 | 612 | 0.7235 | 0.5680 | 0.5175 | 0.6900 | | 0.6033 | 613 | 0.9613 | - | - | - | | 0.6043 | 614 | 0.7124 | - | - | - | | 0.6053 | 615 | 1.0719 | - | - | - | | 0.6063 | 616 | 0.7233 | - | - | - | | 0.6073 | 617 | 1.6863 | - | - | - | | 0.6083 | 618 | 0.8665 | - | - | - | | 0.6093 | 619 | 1.6432 | - | - | - | | 0.6102 | 620 | 0.771 | - | - | - | | 0.6112 | 621 | 0.6755 | - | - | - | | 0.6122 | 622 | 0.6809 | - | - | - | | 0.6132 | 623 | 0.5626 | - | - | - | | 0.6142 | 624 | 0.6287 | - | - | - | | 0.6152 | 625 | 0.5478 | - | - | - | | 0.6161 | 626 | 0.9155 | - | - | - | | 0.6171 | 627 | 1.8947 | - | - | - | | 0.6181 | 628 | 1.1943 | - | - | - | | 0.6191 | 629 | 1.2465 | - | - | - | | 0.6201 | 630 | 0.4045 | - | - | - | | 0.6211 | 631 | 0.5688 | - | - | - | | 0.6220 | 632 | 0.8682 | - | - | - | | 0.6230 | 633 | 0.901 | - | - | - | | 0.6240 | 634 | 0.8978 | - | - | - | | 0.625 | 635 | 0.6592 | - | - | - | | 0.6260 | 636 | 0.6828 | - | - | - | | 0.6270 | 637 | 0.3995 | - | - | - | | 0.6280 | 638 | 0.8283 | - | - | - | | 0.6289 | 639 | 0.5938 | - | - | - | | 0.6299 | 640 | 1.256 | - | - | - | | 0.6309 | 641 | 0.7801 | - | - | - | | 0.6319 | 642 | 0.8975 | - | - | - | | 0.6329 | 643 | 1.255 | - | - | - | | 0.6339 | 644 | 1.1252 | - | - | - | | 0.6348 | 645 | 1.2394 | - | - | - | | 0.6358 | 646 | 0.9579 | - | - | - | | 0.6368 | 647 | 0.8664 | - | - | - | | 0.6378 | 648 | 0.4087 | - | - | - | | 0.6388 | 649 | 0.4084 | - | - | - | | 0.6398 | 650 | 0.6214 | - | - | - | | 0.6407 | 651 | 0.7237 | - | - | - | | 0.6417 | 652 | 0.6294 | - | - | - | | 0.6427 | 653 | 0.8264 | - | - | - | | 0.6437 | 654 | 0.8258 | - | - | - | | 0.6447 | 655 | 0.6865 | - | - | - | | 0.6457 | 656 | 0.7408 | - | - | - | | 0.6467 | 657 | 0.6196 | - | - | - | | 0.6476 | 658 | 0.5659 | - | - | - | | 0.6486 | 659 | 0.6353 | - | - | - | | 0.6496 | 660 | 0.8432 | - | - | - | | 0.6506 | 661 | 0.6573 | - | - | - | | 0.6516 | 662 | 0.4918 | - | - | - | | 0.6526 | 663 | 0.8571 | - | - | - | | 0.6535 | 664 | 0.9105 | - | - | - | | 0.6545 | 665 | 0.7499 | - | - | - | | 0.6555 | 666 | 0.5277 | - | - | - | | 0.6565 | 667 | 1.3046 | - | - | - | | 0.6575 | 668 | 0.6112 | - | - | - | | 0.6585 | 669 | 0.754 | - | - | - | | 0.6594 | 670 | 1.3611 | - | - | - | | 0.6604 | 671 | 1.3654 | - | - | - | | 0.6614 | 672 | 0.6479 | - | - | - | | 0.6624 | 673 | 0.9422 | - | - | - | | 0.6634 | 674 | 0.5007 | - | - | - | | 0.6644 | 675 | 0.4789 | - | - | - | | 0.6654 | 676 | 0.3962 | - | - | - | | 0.6663 | 677 | 0.8648 | - | - | - | | 0.6673 | 678 | 0.8241 | - | - | - | | 0.6683 | 679 | 0.7923 | - | - | - | | 0.6693 | 680 | 0.5799 | - | - | - | | 0.6703 | 681 | 0.8422 | - | - | - | | 0.6713 | 682 | 0.5638 | - | - | - | | 0.6722 | 683 | 0.9627 | - | - | - | | 0.6732 | 684 | 0.7585 | - | - | - | | 0.6742 | 685 | 0.488 | - | - | - | | 0.6752 | 686 | 1.0991 | - | - | - | | 0.6762 | 687 | 0.4808 | - | - | - | | 0.6772 | 688 | 1.3717 | - | - | - | | 0.6781 | 689 | 0.8225 | - | - | - | | 0.6791 | 690 | 0.9377 | - | - | - | | 0.6801 | 691 | 0.8016 | - | - | - | | 0.6811 | 692 | 0.6257 | - | - | - | | 0.6821 | 693 | 1.0941 | - | - | - | | 0.6831 | 694 | 1.0878 | - | - | - | | 0.6841 | 695 | 0.9231 | - | - | - | | 0.6850 | 696 | 1.3284 | - | - | - | | 0.6860 | 697 | 0.7274 | - | - | - | | 0.6870 | 698 | 0.7851 | - | - | - | | 0.6880 | 699 | 0.6807 | - | - | - | | 0.6890 | 700 | 1.1592 | - | - | - | | 0.6900 | 701 | 0.6347 | - | - | - | | 0.6909 | 702 | 1.1326 | - | - | - | | 0.6919 | 703 | 1.3797 | - | - | - | | 0.6929 | 704 | 0.7782 | - | - | - | | 0.6939 | 705 | 1.1746 | - | - | - | | 0.6949 | 706 | 0.7624 | - | - | - | | 0.6959 | 707 | 0.7216 | - | - | - | | 0.6969 | 708 | 0.4666 | - | - | - | | 0.6978 | 709 | 0.9398 | - | - | - | | 0.6988 | 710 | 0.9532 | - | - | - | | 0.6998 | 711 | 0.8285 | - | - | - | | 0.7008 | 712 | 0.7042 | - | - | - | | 0.7018 | 713 | 1.0057 | - | - | - | | 0.7028 | 714 | 0.6696 | - | - | - | | 0.7037 | 715 | 0.5701 | - | - | - | | 0.7047 | 716 | 0.7894 | - | - | - | | 0.7057 | 717 | 0.9327 | - | - | - | | 0.7067 | 718 | 1.0283 | - | - | - | | 0.7077 | 719 | 0.6419 | - | - | - | | 0.7087 | 720 | 0.7716 | - | - | - | | 0.7096 | 721 | 0.5968 | - | - | - | | 0.7106 | 722 | 0.6547 | - | - | - | | 0.7116 | 723 | 0.8344 | - | - | - | | 0.7126 | 724 | 0.5448 | - | - | - | | 0.7136 | 725 | 0.6075 | - | - | - | | 0.7146 | 726 | 0.6142 | - | - | - | | 0.7156 | 727 | 0.681 | - | - | - | | 0.7165 | 728 | 0.6137 | - | - | - | | 0.7175 | 729 | 1.4516 | - | - | - | | 0.7185 | 730 | 0.9083 | - | - | - | | 0.7195 | 731 | 0.6369 | - | - | - | | 0.7205 | 732 | 0.8102 | - | - | - | | 0.7215 | 733 | 0.7808 | - | - | - | | 0.7224 | 734 | 0.512 | - | - | - | | 0.7234 | 735 | 1.3589 | - | - | - | | 0.7244 | 736 | 0.6555 | - | - | - | | 0.7254 | 737 | 0.8732 | - | - | - | | 0.7264 | 738 | 0.7591 | - | - | - | | 0.7274 | 739 | 0.8237 | - | - | - | | 0.7283 | 740 | 1.1272 | - | - | - | | 0.7293 | 741 | 0.5353 | - | - | - | | 0.7303 | 742 | 1.1713 | - | - | - | | 0.7313 | 743 | 0.7034 | - | - | - | | 0.7323 | 744 | 0.5118 | - | - | - | | 0.7333 | 745 | 0.7816 | - | - | - | | 0.7343 | 746 | 0.7935 | - | - | - | | 0.7352 | 747 | 1.0736 | - | - | - | | 0.7362 | 748 | 0.6528 | - | - | - | | 0.7372 | 749 | 0.5556 | - | - | - | | 0.7382 | 750 | 0.3339 | - | - | - | | 0.7392 | 751 | 1.0371 | - | - | - | | 0.7402 | 752 | 1.3273 | - | - | - | | 0.7411 | 753 | 0.5307 | - | - | - | | 0.7421 | 754 | 0.908 | - | - | - | | 0.7431 | 755 | 0.5248 | - | - | - | | 0.7441 | 756 | 0.6128 | - | - | - | | 0.7451 | 757 | 1.5065 | - | - | - | | 0.7461 | 758 | 1.2071 | - | - | - | | 0.7470 | 759 | 0.9983 | - | - | - | | 0.7480 | 760 | 1.0378 | - | - | - | | 0.7490 | 761 | 1.0732 | - | - | - | | 0.75 | 762 | 0.8046 | - | - | - | | 0.7510 | 763 | 1.1666 | - | - | - | | 0.7520 | 764 | 0.7042 | - | - | - | | 0.7530 | 765 | 1.4759 | 0.5730 | 0.5105 | 0.6982 | | 0.7539 | 766 | 0.5595 | - | - | - | | 0.7549 | 767 | 0.3646 | - | - | - | | 0.7559 | 768 | 0.6791 | - | - | - | | 0.7569 | 769 | 0.6889 | - | - | - | | 0.7579 | 770 | 0.9715 | - | - | - | | 0.7589 | 771 | 1.2218 | - | - | - | | 0.7598 | 772 | 0.3957 | - | - | - | | 0.7608 | 773 | 1.5635 | - | - | - | | 0.7618 | 774 | 0.9081 | - | - | - | | 0.7628 | 775 | 0.8633 | - | - | - | | 0.7638 | 776 | 0.9482 | - | - | - | | 0.7648 | 777 | 0.8021 | - | - | - | | 0.7657 | 778 | 0.9083 | - | - | - | | 0.7667 | 779 | 0.9599 | - | - | - | | 0.7677 | 780 | 0.798 | - | - | - | | 0.7687 | 781 | 0.5817 | - | - | - | | 0.7697 | 782 | 0.5287 | - | - | - | | 0.7707 | 783 | 1.0251 | - | - | - | | 0.7717 | 784 | 0.3995 | - | - | - | | 0.7726 | 785 | 0.8753 | - | - | - | | 0.7736 | 786 | 1.315 | - | - | - | | 0.7746 | 787 | 0.8053 | - | - | - | | 0.7756 | 788 | 0.6032 | - | - | - | | 0.7766 | 789 | 0.8059 | - | - | - | | 0.7776 | 790 | 0.9387 | - | - | - | | 0.7785 | 791 | 0.8321 | - | - | - | | 0.7795 | 792 | 0.5788 | - | - | - | | 0.7805 | 793 | 0.7874 | - | - | - | | 0.7815 | 794 | 0.5145 | - | - | - | | 0.7825 | 795 | 1.5847 | - | - | - | | 0.7835 | 796 | 1.1473 | - | - | - | | 0.7844 | 797 | 0.968 | - | - | - | | 0.7854 | 798 | 1.2691 | - | - | - | | 0.7864 | 799 | 0.7046 | - | - | - | | 0.7874 | 800 | 0.9358 | - | - | - | | 0.7884 | 801 | 0.3945 | - | - | - | | 0.7894 | 802 | 0.861 | - | - | - | | 0.7904 | 803 | 1.0736 | - | - | - | | 0.7913 | 804 | 1.0279 | - | - | - | | 0.7923 | 805 | 0.6902 | - | - | - | | 0.7933 | 806 | 0.7344 | - | - | - | | 0.7943 | 807 | 0.8184 | - | - | - | | 0.7953 | 808 | 0.8121 | - | - | - | | 0.7963 | 809 | 0.3772 | - | - | - | | 0.7972 | 810 | 0.2367 | - | - | - | | 0.7982 | 811 | 0.5584 | - | - | - | | 0.7992 | 812 | 0.3465 | - | - | - | | 0.8002 | 813 | 0.9229 | - | - | - | | 0.8012 | 814 | 0.7351 | - | - | - | | 0.8022 | 815 | 0.9024 | - | - | - | | 0.8031 | 816 | 0.4958 | - | - | - | | 0.8041 | 817 | 0.4321 | - | - | - | | 0.8051 | 818 | 0.6945 | - | - | - | | 0.8061 | 819 | 0.6415 | - | - | - | | 0.8071 | 820 | 0.5615 | - | - | - | | 0.8081 | 821 | 0.9732 | - | - | - | | 0.8091 | 822 | 1.018 | - | - | - | | 0.8100 | 823 | 1.1392 | - | - | - | | 0.8110 | 824 | 0.8539 | - | - | - | | 0.8120 | 825 | 0.7081 | - | - | - | | 0.8130 | 826 | 1.3176 | - | - | - | | 0.8140 | 827 | 0.7387 | - | - | - | | 0.8150 | 828 | 1.0975 | - | - | - | | 0.8159 | 829 | 0.5883 | - | - | - | | 0.8169 | 830 | 0.9677 | - | - | - | | 0.8179 | 831 | 0.3869 | - | - | - | | 0.8189 | 832 | 0.9083 | - | - | - | | 0.8199 | 833 | 0.6939 | - | - | - | | 0.8209 | 834 | 0.5046 | - | - | - | | 0.8219 | 835 | 0.3594 | - | - | - | | 0.8228 | 836 | 0.4609 | - | - | - | | 0.8238 | 837 | 0.8132 | - | - | - | | 0.8248 | 838 | 0.4493 | - | - | - | | 0.8258 | 839 | 0.8566 | - | - | - | | 0.8268 | 840 | 0.9958 | - | - | - | | 0.8278 | 841 | 0.936 | - | - | - | | 0.8287 | 842 | 1.4087 | - | - | - | | 0.8297 | 843 | 1.2214 | - | - | - | | 0.8307 | 844 | 0.7135 | - | - | - | | 0.8317 | 845 | 1.1881 | - | - | - | | 0.8327 | 846 | 1.1807 | - | - | - | | 0.8337 | 847 | 0.5896 | - | - | - | | 0.8346 | 848 | 0.8483 | - | - | - | | 0.8356 | 849 | 0.5409 | - | - | - | | 0.8366 | 850 | 0.8286 | - | - | - | | 0.8376 | 851 | 1.136 | - | - | - | | 0.8386 | 852 | 0.819 | - | - | - | | 0.8396 | 853 | 0.3029 | - | - | - | | 0.8406 | 854 | 0.5538 | - | - | - | | 0.8415 | 855 | 0.4572 | - | - | - | | 0.8425 | 856 | 1.1516 | - | - | - | | 0.8435 | 857 | 0.4768 | - | - | - | | 0.8445 | 858 | 0.5357 | - | - | - | | 0.8455 | 859 | 0.8879 | - | - | - | | 0.8465 | 860 | 0.9121 | - | - | - | | 0.8474 | 861 | 1.052 | - | - | - | | 0.8484 | 862 | 0.8009 | - | - | - | | 0.8494 | 863 | 0.2474 | - | - | - | | 0.8504 | 864 | 0.8517 | - | - | - | | 0.8514 | 865 | 0.5678 | - | - | - | | 0.8524 | 866 | 0.4882 | - | - | - | | 0.8533 | 867 | 0.638 | - | - | - | | 0.8543 | 868 | 0.8012 | - | - | - | | 0.8553 | 869 | 0.4172 | - | - | - | | 0.8563 | 870 | 0.6727 | - | - | - | | 0.8573 | 871 | 0.6383 | - | - | - | | 0.8583 | 872 | 1.0728 | - | - | - | | 0.8593 | 873 | 1.3336 | - | - | - | | 0.8602 | 874 | 1.217 | - | - | - | | 0.8612 | 875 | 0.8018 | - | - | - | | 0.8622 | 876 | 1.2326 | - | - | - | | 0.8632 | 877 | 1.0898 | - | - | - | | 0.8642 | 878 | 0.7192 | - | - | - | | 0.8652 | 879 | 0.7779 | - | - | - | | 0.8661 | 880 | 0.9291 | - | - | - | | 0.8671 | 881 | 0.6333 | - | - | - | | 0.8681 | 882 | 0.9028 | - | - | - | | 0.8691 | 883 | 0.9904 | - | - | - | | 0.8701 | 884 | 0.5775 | - | - | - | | 0.8711 | 885 | 0.9783 | - | - | - | | 0.8720 | 886 | 1.111 | - | - | - | | 0.8730 | 887 | 1.0398 | - | - | - | | 0.8740 | 888 | 0.742 | - | - | - | | 0.875 | 889 | 0.7606 | - | - | - | | 0.8760 | 890 | 0.6941 | - | - | - | | 0.8770 | 891 | 1.0219 | - | - | - | | 0.8780 | 892 | 0.8949 | - | - | - | | 0.8789 | 893 | 1.0013 | - | - | - | | 0.8799 | 894 | 1.1122 | - | - | - | | 0.8809 | 895 | 1.1711 | - | - | - | | 0.8819 | 896 | 0.7457 | - | - | - | | 0.8829 | 897 | 0.5431 | - | - | - | | 0.8839 | 898 | 0.8159 | - | - | - | | 0.8848 | 899 | 1.6812 | - | - | - | | 0.8858 | 900 | 0.8163 | - | - | - | | 0.8868 | 901 | 0.9315 | - | - | - | | 0.8878 | 902 | 0.883 | - | - | - | | 0.8888 | 903 | 0.8014 | - | - | - | | 0.8898 | 904 | 1.4277 | - | - | - | | 0.8907 | 905 | 0.7507 | - | - | - | | 0.8917 | 906 | 0.9757 | - | - | - | | 0.8927 | 907 | 0.6366 | - | - | - | | 0.8937 | 908 | 0.7567 | - | - | - | | 0.8947 | 909 | 0.7853 | - | - | - | | 0.8957 | 910 | 0.7611 | - | - | - | | 0.8967 | 911 | 0.8417 | - | - | - | | 0.8976 | 912 | 0.5825 | - | - | - | | 0.8986 | 913 | 1.0816 | - | - | - | | 0.8996 | 914 | 1.1768 | - | - | - | | 0.9006 | 915 | 0.4894 | - | - | - |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.1 - Transformers: 4.44.2 - PyTorch: 2.5.0+cu121 - Accelerate: 0.34.2 - Datasets: 3.0.2 - 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", } ``` #### GISTEmbedLoss ```bibtex @misc{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, year={2024}, eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```