--- base_model: microsoft/deberta-v3-small datasets: - tals/vitaminc language: - en 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:225247 - loss:CachedGISTEmbedLoss widget: - source_sentence: how long to grill boneless skinless chicken breasts in oven sentences: - "[ syll. a-ka-hi, ak-ahi ] The baby boy name Akahi is also used as a girl name.\ \ Its pronunciation is AA K AA HHiy â\x80 . Akahi's origin, as well as its use,\ \ is in the Hawaiian language. The name's meaning is never before. Akahi is infrequently\ \ used as a baby name for boys." - October consists of 31 days. November has 30 days. When you add both together they have 61 days. - Heat a grill or grill pan. When the grill is hot, place the chicken on the grill and cook for about 4 minutes per side, or until cooked through. You can also bake the thawed chicken in a 375 degree F oven for 15 minutes, or until cooked through. - source_sentence: More than 273 people have died from the 2019-20 coronavirus outside mainland China . sentences: - 'More than 3,700 people have died : around 3,100 in mainland China and around 550 in all other countries combined .' - 'More than 3,200 people have died : almost 3,000 in mainland China and around 275 in other countries .' - more than 4,900 deaths have been attributed to COVID-19 . - source_sentence: Most red algae species live in oceans. sentences: - Where do most red algae species live? - Which layer of the earth is molten? - As a diver descends, the increase in pressure causes the body’s air pockets in the ears and lungs to do what? - source_sentence: Binary compounds of carbon with less electronegative elements are called carbides. sentences: - What are four children born at one birth called? - Binary compounds of carbon with less electronegative elements are called what? - The water cycle involves movement of water between air and what? - source_sentence: What is the basic monetary unit of Iceland? sentences: - 'Ao dai - Vietnamese traditional dress - YouTube Ao dai - Vietnamese traditional dress Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Uploaded on Jul 8, 2009 Simple, yet charming, graceful and elegant, áo dài was designed to praise the slender beauty of Vietnamese women. The dress is a genius combination of ancient and modern. It shows every curve on the girl''s body, creating sexiness for the wearer, yet it still preserves the traditional feminine grace of Vietnamese women with its charming flowing flaps. The simplicity of áo dài makes it convenient and practical, something that other Asian traditional clothes lack. The waist-length slits of the flaps allow every movement of the legs: walking, running, riding a bicycle, climbing a tree, doing high kicks. The looseness of the pants allows comfortability. As a girl walks in áo dài, the movements of the flaps make it seem like she''s not walking but floating in the air. This breath-taking beautiful image of a Vietnamese girl walking in áo dài has been an inspiration for generations of Vietnamese poets, novelists, artists and has left a deep impression for every foreigner who has visited the country. Category' - 'Icelandic monetary unit - definition of Icelandic monetary unit by The Free Dictionary Icelandic monetary unit - definition of Icelandic monetary unit by The Free Dictionary http://www.thefreedictionary.com/Icelandic+monetary+unit Related to Icelandic monetary unit: Icelandic Old Krona ThesaurusAntonymsRelated WordsSynonymsLegend: monetary unit - a unit of money Icelandic krona , krona - the basic unit of money in Iceland eyrir - 100 aurar equal 1 krona in Iceland Want to thank TFD for its existence? Tell a friend about us , add a link to this page, or visit the webmaster''s page for free fun content . Link to this page: Copyright © 2003-2017 Farlex, Inc Disclaimer All content on this website, including dictionary, thesaurus, literature, geography, and other reference data is for informational purposes only. This information should not be considered complete, up to date, and is not intended to be used in place of a visit, consultation, or advice of a legal, medical, or any other professional.' - 'Food-Info.net : E-numbers : E140: Chlorophyll CI 75810, Natural Green 3, Chlorophyll A, Magnesium chlorophyll Origin: Natural green colour, present in all plants and algae. Commercially extracted from nettles, grass and alfalfa. Function & characteristics:' 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: 0.3977846210139704 name: Pearson Cosine - type: spearman_cosine value: 0.44299644096637864 name: Spearman Cosine - type: pearson_manhattan value: 0.43174431600737306 name: Pearson Manhattan - type: spearman_manhattan value: 0.4553695033739603 name: Spearman Manhattan - type: pearson_euclidean value: 0.42060129087924125 name: Pearson Euclidean - type: spearman_euclidean value: 0.44300328790921845 name: Spearman Euclidean - type: pearson_dot value: 0.3974381713503513 name: Pearson Dot - type: spearman_dot value: 0.4426330607320026 name: Spearman Dot - type: pearson_max value: 0.43174431600737306 name: Pearson Max - type: spearman_max value: 0.4553695033739603 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: allNLI dev type: allNLI-dev metrics: - type: cosine_accuracy value: 0.66796875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.9727417230606079 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.5338983050847458 name: Cosine F1 - type: cosine_f1_threshold value: 0.8509687781333923 name: Cosine F1 Threshold - type: cosine_precision value: 0.4214046822742475 name: Cosine Precision - type: cosine_recall value: 0.7283236994219653 name: Cosine Recall - type: cosine_ap value: 0.4443750308487611 name: Cosine Ap - type: dot_accuracy value: 0.66796875 name: Dot Accuracy - type: dot_accuracy_threshold value: 747.4664916992188 name: Dot Accuracy Threshold - type: dot_f1 value: 0.5347368421052632 name: Dot F1 - type: dot_f1_threshold value: 652.6121826171875 name: Dot F1 Threshold - type: dot_precision value: 0.4205298013245033 name: Dot Precision - type: dot_recall value: 0.7341040462427746 name: Dot Recall - type: dot_ap value: 0.4447331164315086 name: Dot Ap - type: manhattan_accuracy value: 0.673828125 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 185.35494995117188 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.5340909090909091 name: Manhattan F1 - type: manhattan_f1_threshold value: 316.48419189453125 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.3971830985915493 name: Manhattan Precision - type: manhattan_recall value: 0.815028901734104 name: Manhattan Recall - type: manhattan_ap value: 0.45330636568192945 name: Manhattan Ap - type: euclidean_accuracy value: 0.66796875 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 6.472302436828613 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.5338983050847458 name: Euclidean F1 - type: euclidean_f1_threshold value: 15.134000778198242 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.4214046822742475 name: Euclidean Precision - type: euclidean_recall value: 0.7283236994219653 name: Euclidean Recall - type: euclidean_ap value: 0.44436910603457025 name: Euclidean Ap - type: max_accuracy value: 0.673828125 name: Max Accuracy - type: max_accuracy_threshold value: 747.4664916992188 name: Max Accuracy Threshold - type: max_f1 value: 0.5347368421052632 name: Max F1 - type: max_f1_threshold value: 652.6121826171875 name: Max F1 Threshold - type: max_precision value: 0.4214046822742475 name: Max Precision - type: max_recall value: 0.815028901734104 name: Max Recall - type: max_ap value: 0.45330636568192945 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Qnli dev type: Qnli-dev metrics: - type: cosine_accuracy value: 0.66015625 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8744948506355286 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6646433990895295 name: Cosine F1 - type: cosine_f1_threshold value: 0.753309965133667 name: Cosine F1 Threshold - type: cosine_precision value: 0.5177304964539007 name: Cosine Precision - type: cosine_recall value: 0.9279661016949152 name: Cosine Recall - type: cosine_ap value: 0.6610633478265061 name: Cosine Ap - type: dot_accuracy value: 0.66015625 name: Dot Accuracy - type: dot_accuracy_threshold value: 670.719970703125 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6646433990895295 name: Dot F1 - type: dot_f1_threshold value: 578.874755859375 name: Dot F1 Threshold - type: dot_precision value: 0.5177304964539007 name: Dot Precision - type: dot_recall value: 0.9279661016949152 name: Dot Recall - type: dot_ap value: 0.6607472505349153 name: Dot Ap - type: manhattan_accuracy value: 0.666015625 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 281.9825134277344 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6678899082568808 name: Manhattan F1 - type: manhattan_f1_threshold value: 328.83447265625 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5889967637540453 name: Manhattan Precision - type: manhattan_recall value: 0.7711864406779662 name: Manhattan Recall - type: manhattan_ap value: 0.6664006509577655 name: Manhattan Ap - type: euclidean_accuracy value: 0.66015625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 13.881525039672852 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6646433990895295 name: Euclidean F1 - type: euclidean_f1_threshold value: 19.471359252929688 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5177304964539007 name: Euclidean Precision - type: euclidean_recall value: 0.9279661016949152 name: Euclidean Recall - type: euclidean_ap value: 0.6611053426809266 name: Euclidean Ap - type: max_accuracy value: 0.666015625 name: Max Accuracy - type: max_accuracy_threshold value: 670.719970703125 name: Max Accuracy Threshold - type: max_f1 value: 0.6678899082568808 name: Max F1 - type: max_f1_threshold value: 578.874755859375 name: Max F1 Threshold - type: max_precision value: 0.5889967637540453 name: Max Precision - type: max_recall value: 0.9279661016949152 name: Max Recall - type: max_ap value: 0.6664006509577655 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 - **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': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): AdvancedWeightedPooling( (linear_cls): Linear(in_features=768, out_features=768, bias=True) (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) ) (layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm2): 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-CustomPooling-test1-checkpoints-tmp") # Run inference sentences = [ 'What is the basic monetary unit of Iceland?', "Icelandic monetary unit - definition of Icelandic monetary unit by The Free Dictionary Icelandic monetary unit - definition of Icelandic monetary unit by The Free Dictionary http://www.thefreedictionary.com/Icelandic+monetary+unit Related to Icelandic monetary unit: Icelandic Old Krona ThesaurusAntonymsRelated WordsSynonymsLegend: monetary unit - a unit of money Icelandic krona , krona - the basic unit of money in Iceland eyrir - 100 aurar equal 1 krona in Iceland Want to thank TFD for its existence? Tell a friend about us , add a link to this page, or visit the webmaster's page for free fun content . Link to this page: Copyright © 2003-2017 Farlex, Inc Disclaimer All content on this website, including dictionary, thesaurus, literature, geography, and other reference data is for informational purposes only. This information should not be considered complete, up to date, and is not intended to be used in place of a visit, consultation, or advice of a legal, medical, or any other professional.", 'Food-Info.net : E-numbers : E140: Chlorophyll CI 75810, Natural Green 3, Chlorophyll A, Magnesium chlorophyll Origin: Natural green colour, present in all plants and algae. Commercially extracted from nettles, grass and alfalfa. Function & characteristics:', ] 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 | 0.3978 | | **spearman_cosine** | **0.443** | | pearson_manhattan | 0.4317 | | spearman_manhattan | 0.4554 | | pearson_euclidean | 0.4206 | | spearman_euclidean | 0.443 | | pearson_dot | 0.3974 | | spearman_dot | 0.4426 | | pearson_max | 0.4317 | | spearman_max | 0.4554 | #### 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 | 0.668 | | cosine_accuracy_threshold | 0.9727 | | cosine_f1 | 0.5339 | | cosine_f1_threshold | 0.851 | | cosine_precision | 0.4214 | | cosine_recall | 0.7283 | | cosine_ap | 0.4444 | | dot_accuracy | 0.668 | | dot_accuracy_threshold | 747.4665 | | dot_f1 | 0.5347 | | dot_f1_threshold | 652.6122 | | dot_precision | 0.4205 | | dot_recall | 0.7341 | | dot_ap | 0.4447 | | manhattan_accuracy | 0.6738 | | manhattan_accuracy_threshold | 185.3549 | | manhattan_f1 | 0.5341 | | manhattan_f1_threshold | 316.4842 | | manhattan_precision | 0.3972 | | manhattan_recall | 0.815 | | manhattan_ap | 0.4533 | | euclidean_accuracy | 0.668 | | euclidean_accuracy_threshold | 6.4723 | | euclidean_f1 | 0.5339 | | euclidean_f1_threshold | 15.134 | | euclidean_precision | 0.4214 | | euclidean_recall | 0.7283 | | euclidean_ap | 0.4444 | | max_accuracy | 0.6738 | | max_accuracy_threshold | 747.4665 | | max_f1 | 0.5347 | | max_f1_threshold | 652.6122 | | max_precision | 0.4214 | | max_recall | 0.815 | | **max_ap** | **0.4533** | #### 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 | 0.6602 | | cosine_accuracy_threshold | 0.8745 | | cosine_f1 | 0.6646 | | cosine_f1_threshold | 0.7533 | | cosine_precision | 0.5177 | | cosine_recall | 0.928 | | cosine_ap | 0.6611 | | dot_accuracy | 0.6602 | | dot_accuracy_threshold | 670.72 | | dot_f1 | 0.6646 | | dot_f1_threshold | 578.8748 | | dot_precision | 0.5177 | | dot_recall | 0.928 | | dot_ap | 0.6607 | | manhattan_accuracy | 0.666 | | manhattan_accuracy_threshold | 281.9825 | | manhattan_f1 | 0.6679 | | manhattan_f1_threshold | 328.8345 | | manhattan_precision | 0.589 | | manhattan_recall | 0.7712 | | manhattan_ap | 0.6664 | | euclidean_accuracy | 0.6602 | | euclidean_accuracy_threshold | 13.8815 | | euclidean_f1 | 0.6646 | | euclidean_f1_threshold | 19.4714 | | euclidean_precision | 0.5177 | | euclidean_recall | 0.928 | | euclidean_ap | 0.6611 | | max_accuracy | 0.666 | | max_accuracy_threshold | 670.72 | | max_f1 | 0.6679 | | max_f1_threshold | 578.8748 | | max_precision | 0.589 | | max_recall | 0.928 | | **max_ap** | **0.6664** | ## Training Details ### Evaluation Dataset #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 128 evaluation samples * Columns: claim and evidence * Approximate statistics based on the first 128 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dragon Con had over 5000 guests . | Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell . | | COVID-19 has reached more than 185 countries . | As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths . | | In March , Italy had 3.6x times more cases of coronavirus than China . | As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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`: 42 - `per_device_eval_batch_size`: 128 - `gradient_accumulation_steps`: 2 - `learning_rate`: 3e-05 - `weight_decay`: 0.001 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1e-05} - `warmup_ratio`: 0.25 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa3-s-CustomPooling-test1-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`: 42 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 3e-05 - `weight_decay`: 0.001 - `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': 1e-05} - `warmup_ratio`: 0.25 - `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-CustomPooling-test1-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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | vitaminc-pairs loss | negation-triplets loss | scitail-pairs-pos loss | scitail-pairs-qa loss | xsum-pairs loss | sciq pairs loss | qasc pairs loss | openbookqa pairs loss | msmarco pairs loss | nq pairs loss | trivia pairs loss | gooaq pairs loss | paws-pos loss | global dataset loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap | |:------:|:----:|:-------------:|:-------------------:|:----------------------:|:----------------------:|:---------------------:|:---------------:|:---------------:|:---------------:|:---------------------:|:------------------:|:-------------:|:-----------------:|:----------------:|:-------------:|:-------------------:|:------------------------:|:-----------------:|:---------------:| | 0.0009 | 1 | 5.8564 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0018 | 2 | 7.1716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0027 | 3 | 5.9095 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0035 | 4 | 5.0841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0044 | 5 | 4.0184 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0053 | 6 | 6.2191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0062 | 7 | 5.6124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0071 | 8 | 3.9544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0080 | 9 | 4.7149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0088 | 10 | 4.9616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0097 | 11 | 5.2794 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0106 | 12 | 8.8704 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0115 | 13 | 6.0707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0124 | 14 | 5.4071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0133 | 15 | 6.9104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0142 | 16 | 6.0276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0150 | 17 | 6.737 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0159 | 18 | 6.5354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0168 | 19 | 5.206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0177 | 20 | 5.2469 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0186 | 21 | 5.3771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0195 | 22 | 4.979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0204 | 23 | 4.7909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0212 | 24 | 4.9086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0221 | 25 | 4.8826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0230 | 26 | 8.2266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0239 | 27 | 8.3024 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0248 | 28 | 5.8745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0257 | 29 | 4.7298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0265 | 30 | 5.4614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0274 | 31 | 5.8594 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0283 | 32 | 5.2401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0292 | 33 | 5.1579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0301 | 34 | 5.2181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0310 | 35 | 4.6328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0319 | 36 | 2.121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0327 | 37 | 5.9026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0336 | 38 | 7.3796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0345 | 39 | 5.5361 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0354 | 40 | 4.0243 | 2.9018 | 5.6903 | 2.1136 | 2.8052 | 6.5831 | 0.8882 | 4.1148 | 5.0966 | 10.3911 | 10.9032 | 7.1904 | 8.1935 | 1.3943 | 5.6716 | 0.1879 | 0.3385 | 0.5781 | | 0.0363 | 41 | 4.9072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0372 | 42 | 3.4439 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0381 | 43 | 4.9787 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0389 | 44 | 5.8318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0398 | 45 | 5.3226 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0407 | 46 | 5.1181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0416 | 47 | 4.7834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0425 | 48 | 6.6303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0434 | 49 | 5.8171 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0442 | 50 | 5.1962 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0451 | 51 | 5.2096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0460 | 52 | 5.0943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0469 | 53 | 4.9038 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0478 | 54 | 4.6479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0487 | 55 | 5.5098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0496 | 56 | 4.6979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0504 | 57 | 3.1969 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0513 | 58 | 4.4127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0522 | 59 | 3.7746 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0531 | 60 | 4.5378 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0540 | 61 | 5.0209 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0549 | 62 | 6.5936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0558 | 63 | 4.2315 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0566 | 64 | 6.4269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0575 | 65 | 4.2644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0584 | 66 | 5.1388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0593 | 67 | 5.1852 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0602 | 68 | 4.8057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0611 | 69 | 3.1725 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0619 | 70 | 3.3322 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0628 | 71 | 5.139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0637 | 72 | 4.307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0646 | 73 | 5.0133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0655 | 74 | 4.0507 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0664 | 75 | 3.3895 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0673 | 76 | 5.6736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0681 | 77 | 4.2572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0690 | 78 | 3.0796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0699 | 79 | 5.0199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0708 | 80 | 4.1414 | 2.7794 | 4.8890 | 1.8997 | 2.6761 | 6.2096 | 0.7622 | 3.3129 | 4.5498 | 7.2056 | 7.6809 | 6.3792 | 6.6567 | 1.3848 | 5.0030 | 0.2480 | 0.3513 | 0.5898 | | 0.0717 | 81 | 5.8604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0726 | 82 | 4.3003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0735 | 83 | 4.4568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0743 | 84 | 4.2747 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0752 | 85 | 5.52 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0761 | 86 | 2.7767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0770 | 87 | 4.397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0779 | 88 | 5.4449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0788 | 89 | 4.2706 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0796 | 90 | 6.4759 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0805 | 91 | 4.1951 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0814 | 92 | 4.6328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0823 | 93 | 4.1278 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0832 | 94 | 4.1787 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0841 | 95 | 5.2156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0850 | 96 | 3.1403 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0858 | 97 | 4.0273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0867 | 98 | 3.0624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0876 | 99 | 4.6786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0885 | 100 | 4.1505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0894 | 101 | 2.9529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0903 | 102 | 4.7048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0912 | 103 | 4.7388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0920 | 104 | 3.7879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0929 | 105 | 4.0311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0938 | 106 | 4.1314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0947 | 107 | 4.9411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0956 | 108 | 4.1118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0965 | 109 | 3.6971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0973 | 110 | 5.605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0982 | 111 | 3.4563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0991 | 112 | 3.7422 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1 | 113 | 3.8055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1009 | 114 | 5.2369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1018 | 115 | 5.6518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1027 | 116 | 3.2906 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1035 | 117 | 3.4996 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1044 | 118 | 3.6283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1053 | 119 | 4.1487 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1062 | 120 | 4.3996 | 2.7279 | 4.3946 | 1.4130 | 2.1150 | 6.0486 | 0.7172 | 2.9669 | 4.4180 | 6.3022 | 6.8412 | 6.2013 | 6.0982 | 0.9474 | 4.3852 | 0.3149 | 0.3693 | 0.5975 | | 0.1071 | 121 | 3.5291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1080 | 122 | 3.8232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1088 | 123 | 4.6035 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1097 | 124 | 3.7607 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1106 | 125 | 3.8461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1115 | 126 | 3.3413 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1124 | 127 | 4.2777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1133 | 128 | 4.3597 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1142 | 129 | 3.9046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1150 | 130 | 4.0527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1159 | 131 | 5.0883 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1168 | 132 | 3.8308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1177 | 133 | 3.572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1186 | 134 | 3.4299 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1195 | 135 | 4.1541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1204 | 136 | 3.584 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1212 | 137 | 5.0977 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1221 | 138 | 4.6769 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1230 | 139 | 3.8396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1239 | 140 | 3.2875 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1248 | 141 | 4.1946 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1257 | 142 | 4.9602 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1265 | 143 | 4.1531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1274 | 144 | 3.8351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1283 | 145 | 3.112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1292 | 146 | 2.3145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1301 | 147 | 4.0989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1310 | 148 | 3.2173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1319 | 149 | 2.7913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1327 | 150 | 3.7627 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1336 | 151 | 3.3669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1345 | 152 | 2.6775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1354 | 153 | 3.2804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1363 | 154 | 3.0676 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1372 | 155 | 3.1559 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1381 | 156 | 2.6638 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1389 | 157 | 2.8045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1398 | 158 | 4.0568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1407 | 159 | 2.7554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1416 | 160 | 3.7407 | 2.7439 | 4.6364 | 1.0089 | 1.1229 | 5.4870 | 0.6284 | 2.5933 | 4.3943 | 5.6565 | 5.9870 | 5.6944 | 5.3857 | 0.3622 | 3.4011 | 0.3141 | 0.3898 | 0.6417 | | 0.1425 | 161 | 3.4324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1434 | 162 | 3.6658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1442 | 163 | 3.96 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1451 | 164 | 2.3167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1460 | 165 | 3.6345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1469 | 166 | 2.462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1478 | 167 | 1.4742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1487 | 168 | 4.7312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1496 | 169 | 2.6785 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1504 | 170 | 3.449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1513 | 171 | 2.437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1522 | 172 | 4.2431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1531 | 173 | 4.4848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1540 | 174 | 2.5575 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1549 | 175 | 2.3798 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1558 | 176 | 4.4939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1566 | 177 | 4.1285 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1575 | 178 | 3.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1584 | 179 | 4.4431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1593 | 180 | 3.1172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1602 | 181 | 2.3576 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1611 | 182 | 3.7849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1619 | 183 | 3.679 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1628 | 184 | 3.1949 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1637 | 185 | 3.2422 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1646 | 186 | 2.9905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1655 | 187 | 2.2697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1664 | 188 | 1.7685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1673 | 189 | 2.0971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1681 | 190 | 3.4689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1690 | 191 | 1.6614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1699 | 192 | 1.9574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1708 | 193 | 1.9313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1717 | 194 | 2.2316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1726 | 195 | 1.9854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1735 | 196 | 2.8428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1743 | 197 | 2.6916 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1752 | 198 | 3.5193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1761 | 199 | 3.1681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1770 | 200 | 2.7377 | 2.7042 | 4.8735 | 0.6428 | 0.6248 | 4.3639 | 0.4776 | 1.8950 | 3.3982 | 4.1048 | 4.7591 | 4.4568 | 4.1613 | 0.1802 | 2.4959 | 0.3521 | 0.4227 | 0.6702 | | 0.1779 | 201 | 1.6408 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1788 | 202 | 2.3864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1796 | 203 | 2.0848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1805 | 204 | 2.9074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1814 | 205 | 2.542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1823 | 206 | 1.7312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1832 | 207 | 1.6768 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1841 | 208 | 2.531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1850 | 209 | 2.9222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1858 | 210 | 2.4152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1867 | 211 | 1.4345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1876 | 212 | 1.5864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1885 | 213 | 1.272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1894 | 214 | 1.7011 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1903 | 215 | 3.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1912 | 216 | 2.468 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1920 | 217 | 2.0796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1929 | 218 | 2.9735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1938 | 219 | 2.5506 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1947 | 220 | 1.7307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1956 | 221 | 1.4519 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1965 | 222 | 1.7292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1973 | 223 | 1.4664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1982 | 224 | 1.6201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1991 | 225 | 2.3483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2 | 226 | 2.1311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2009 | 227 | 2.3272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2018 | 228 | 2.6164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2027 | 229 | 1.6261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2035 | 230 | 2.5293 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2044 | 231 | 1.2885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2053 | 232 | 2.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2062 | 233 | 3.0003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2071 | 234 | 2.0491 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2080 | 235 | 2.0178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2088 | 236 | 1.8532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2097 | 237 | 2.3614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2106 | 238 | 1.1889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2115 | 239 | 1.4833 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2124 | 240 | 2.8687 | 2.7215 | 4.1544 | 0.4166 | 0.3876 | 3.3157 | 0.3711 | 1.4818 | 2.6939 | 3.2454 | 3.9798 | 3.5949 | 3.2266 | 0.1275 | 1.8867 | 0.4430 | 0.4533 | 0.6664 |
### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.2.0 - Transformers: 4.45.1 - PyTorch: 2.4.0 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## 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", } ```