--- 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: 0.22248205020578934 name: Pearson Cosine - type: spearman_cosine value: 0.24802235964390085 name: Spearman Cosine - type: pearson_manhattan value: 0.26632593273308647 name: Pearson Manhattan - type: spearman_manhattan value: 0.2843623073856928 name: Spearman Manhattan - type: pearson_euclidean value: 0.2323160413842197 name: Pearson Euclidean - type: spearman_euclidean value: 0.24799036249272113 name: Spearman Euclidean - type: pearson_dot value: 0.22239084967931927 name: Pearson Dot - type: spearman_dot value: 0.24791612015173234 name: Spearman Dot - type: pearson_max value: 0.26632593273308647 name: Pearson Max - type: spearman_max value: 0.2843623073856928 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: allNLI dev type: allNLI-dev metrics: - type: cosine_accuracy value: 0.666015625 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.983686089515686 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.5065885797950219 name: Cosine F1 - type: cosine_f1_threshold value: 0.7642872333526611 name: Cosine F1 Threshold - type: cosine_precision value: 0.3392156862745098 name: Cosine Precision - type: cosine_recall value: 1.0 name: Cosine Recall - type: cosine_ap value: 0.34411819659341086 name: Cosine Ap - type: dot_accuracy value: 0.666015625 name: Dot Accuracy - type: dot_accuracy_threshold value: 755.60302734375 name: Dot Accuracy Threshold - type: dot_f1 value: 0.5065885797950219 name: Dot F1 - type: dot_f1_threshold value: 587.0625 name: Dot F1 Threshold - type: dot_precision value: 0.3392156862745098 name: Dot Precision - type: dot_recall value: 1.0 name: Dot Recall - type: dot_ap value: 0.344109544232086 name: Dot Ap - type: manhattan_accuracy value: 0.6640625 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 62.69102096557617 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.5058479532163743 name: Manhattan F1 - type: manhattan_f1_threshold value: 337.6861877441406 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.3385518590998043 name: Manhattan Precision - type: manhattan_recall value: 1.0 name: Manhattan Recall - type: manhattan_ap value: 0.35131239981425566 name: Manhattan Ap - type: euclidean_accuracy value: 0.666015625 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 5.00581693649292 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.5065885797950219 name: Euclidean F1 - type: euclidean_f1_threshold value: 19.022436141967773 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.3392156862745098 name: Euclidean Precision - type: euclidean_recall value: 1.0 name: Euclidean Recall - type: euclidean_ap value: 0.3441246898925644 name: Euclidean Ap - type: max_accuracy value: 0.666015625 name: Max Accuracy - type: max_accuracy_threshold value: 755.60302734375 name: Max Accuracy Threshold - type: max_f1 value: 0.5065885797950219 name: Max F1 - type: max_f1_threshold value: 587.0625 name: Max F1 Threshold - type: max_precision value: 0.3392156862745098 name: Max Precision - type: max_recall value: 1.0 name: Max Recall - type: max_ap value: 0.35131239981425566 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Qnli dev type: Qnli-dev metrics: - type: cosine_accuracy value: 0.591796875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.9258557558059692 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6291834002677376 name: Cosine F1 - type: cosine_f1_threshold value: 0.750666618347168 name: Cosine F1 Threshold - type: cosine_precision value: 0.4598825831702544 name: Cosine Precision - type: cosine_recall value: 0.9957627118644068 name: Cosine Recall - type: cosine_ap value: 0.5585355274462735 name: Cosine Ap - type: dot_accuracy value: 0.591796875 name: Dot Accuracy - type: dot_accuracy_threshold value: 711.18359375 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6291834002677376 name: Dot F1 - type: dot_f1_threshold value: 576.5970458984375 name: Dot F1 Threshold - type: dot_precision value: 0.4598825831702544 name: Dot Precision - type: dot_recall value: 0.9957627118644068 name: Dot Recall - type: dot_ap value: 0.5585297234749824 name: Dot Ap - type: manhattan_accuracy value: 0.619140625 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 188.09068298339844 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6301775147928994 name: Manhattan F1 - type: manhattan_f1_threshold value: 237.80462646484375 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.48409090909090907 name: Manhattan Precision - type: manhattan_recall value: 0.902542372881356 name: Manhattan Recall - type: manhattan_ap value: 0.5898283705050701 name: Manhattan Ap - type: euclidean_accuracy value: 0.591796875 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 10.672666549682617 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6291834002677376 name: Euclidean F1 - type: euclidean_f1_threshold value: 19.553747177124023 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.4598825831702544 name: Euclidean Precision - type: euclidean_recall value: 0.9957627118644068 name: Euclidean Recall - type: euclidean_ap value: 0.5585355274462735 name: Euclidean Ap - type: max_accuracy value: 0.619140625 name: Max Accuracy - type: max_accuracy_threshold value: 711.18359375 name: Max Accuracy Threshold - type: max_f1 value: 0.6301775147928994 name: Max F1 - type: max_f1_threshold value: 576.5970458984375 name: Max F1 Threshold - type: max_precision value: 0.48409090909090907 name: Max Precision - type: max_recall value: 0.9957627118644068 name: Max Recall - type: max_ap value: 0.5898283705050701 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 | 0.2225 | | **spearman_cosine** | **0.248** | | pearson_manhattan | 0.2663 | | spearman_manhattan | 0.2844 | | pearson_euclidean | 0.2323 | | spearman_euclidean | 0.248 | | pearson_dot | 0.2224 | | spearman_dot | 0.2479 | | pearson_max | 0.2663 | | spearman_max | 0.2844 | #### 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 | 0.666 | | cosine_accuracy_threshold | 0.9837 | | cosine_f1 | 0.5066 | | cosine_f1_threshold | 0.7643 | | cosine_precision | 0.3392 | | cosine_recall | 1.0 | | cosine_ap | 0.3441 | | dot_accuracy | 0.666 | | dot_accuracy_threshold | 755.603 | | dot_f1 | 0.5066 | | dot_f1_threshold | 587.0625 | | dot_precision | 0.3392 | | dot_recall | 1.0 | | dot_ap | 0.3441 | | manhattan_accuracy | 0.6641 | | manhattan_accuracy_threshold | 62.691 | | manhattan_f1 | 0.5058 | | manhattan_f1_threshold | 337.6862 | | manhattan_precision | 0.3386 | | manhattan_recall | 1.0 | | manhattan_ap | 0.3513 | | euclidean_accuracy | 0.666 | | euclidean_accuracy_threshold | 5.0058 | | euclidean_f1 | 0.5066 | | euclidean_f1_threshold | 19.0224 | | euclidean_precision | 0.3392 | | euclidean_recall | 1.0 | | euclidean_ap | 0.3441 | | max_accuracy | 0.666 | | max_accuracy_threshold | 755.603 | | max_f1 | 0.5066 | | max_f1_threshold | 587.0625 | | max_precision | 0.3392 | | max_recall | 1.0 | | **max_ap** | **0.3513** | #### 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 | 0.5918 | | cosine_accuracy_threshold | 0.9259 | | cosine_f1 | 0.6292 | | cosine_f1_threshold | 0.7507 | | cosine_precision | 0.4599 | | cosine_recall | 0.9958 | | cosine_ap | 0.5585 | | dot_accuracy | 0.5918 | | dot_accuracy_threshold | 711.1836 | | dot_f1 | 0.6292 | | dot_f1_threshold | 576.597 | | dot_precision | 0.4599 | | dot_recall | 0.9958 | | dot_ap | 0.5585 | | manhattan_accuracy | 0.6191 | | manhattan_accuracy_threshold | 188.0907 | | manhattan_f1 | 0.6302 | | manhattan_f1_threshold | 237.8046 | | manhattan_precision | 0.4841 | | manhattan_recall | 0.9025 | | manhattan_ap | 0.5898 | | euclidean_accuracy | 0.5918 | | euclidean_accuracy_threshold | 10.6727 | | euclidean_f1 | 0.6292 | | euclidean_f1_threshold | 19.5537 | | euclidean_precision | 0.4599 | | euclidean_recall | 0.9958 | | euclidean_ap | 0.5585 | | max_accuracy | 0.6191 | | max_accuracy_threshold | 711.1836 | | max_f1 | 0.6302 | | max_f1_threshold | 576.597 | | max_precision | 0.4841 | | max_recall | 0.9958 | | **max_ap** | **0.5898** | ## 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 | 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 | ### 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", } ```