sentence-transformers pytorch tensorboard deberta-v2 sentence-similarity feature-extraction generated_from_trainer dataset_size:123245 loss:CachedGISTEmbedLoss en dataset:tals/vitaminc dataset:allenai/scitail dataset:allenai/sciq dataset:allenai/qasc dataset:sentence-transformers/msmarco-msmarco-distilbert-base-v3 dataset:sentence-transformers/natural-questions dataset:sentence-transformers/trivia-qa dataset:sentence-transformers/gooaq dataset:google-research-datasets/paws arxiv:1908.10084 base_model:microsoft/deberta-v3-base base_model:finetune:microsoft/deberta-v3-base model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/deberta-v3-base
datasets:
- tals/vitaminc
- allenai/scitail
- allenai/sciq
- allenai/qasc
- sentence-transformers/msmarco-msmarco-distilbert-base-v3
- sentence-transformers/natural-questions
- sentence-transformers/trivia-qa
- sentence-transformers/gooaq
- google-research-datasets/paws
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:123245
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: what type of inheritance does haemochromatosis
  sentences:
  - Nestled on the tranquil banks of the Pamlico River, Moss Landing is a vibrant
    new community of thoughtfully conceived, meticulously crafted single-family homes
    in Washington, North Carolina. Washington is renowned for its historic architecture
    and natural beauty.
  - '1 Microwave on high for 8 to 10 minutes or until tender, turning the yams once.
    2  To microwave sliced yams: Wash, peel, and cut off the woody portions and ends.
    3  Cut yams into quarters. 4  Place the yams and 1/2 cup water in a microwave-safe
    casserole.ake the Yams. 1  Place half the yams in a 1-quart casserole. 2  Layer
    with half the brown sugar and half the margarine. 3  Repeat the layers. 4  Bake,
    uncovered, in a 375 degree F oven for 30 to 35 minutes or until the yams are glazed,
    spooning the liquid over the yams once or twice during cooking.'
  - Types 1, 2, and 3 hemochromatosis are inherited in an autosomal recessive pattern,
    which means both copies of the gene in each cell have mutations. Most often, the
    parents of an individual with an autosomal recessive condition each carry one
    copy of the mutated gene but do not show signs and symptoms of the condition.Type
    4 hemochromatosis is distinguished by its autosomal dominant inheritance pattern.With
    this type of inheritance, one copy of the altered gene in each cell is sufficient
    to cause the disorder. In most cases, an affected person has one parent with the
    condition.ype 1, the most common form of the disorder, and type 4 (also called
    ferroportin disease) begin in adulthood. Men with type 1 or type 4 hemochromatosis
    typically develop symptoms between the ages of 40 and 60, and women usually develop
    symptoms after menopause. Type 2 hemochromatosis is a juvenile-onset disorder.
- 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: The male reproductive system consists of structures that produce
    sperm and secrete testosterone.
  sentences:
  - What does the male reproductive system consist of?
  - What facilitates the diffusion of ions across a membrane?
  - Autoimmunity can develop with time, and its causes may be rooted in this?
- source_sentence: Nitrogen gas comprises about three-fourths of earth's atmosphere.
  sentences:
  - What do all cells have in common?
  - What gas comprises about three-fourths of earth's atmosphere?
  - What do you call an animal in which the embryo, often termed a joey, is born immature
    and must complete its development outside the mother's body?
- source_sentence: What device is used to regulate a person's heart rate?
  sentences:
  - 'Marie Antoinette and the French Revolution    .   Famous Faces    .   Mad Max:
    Maximilien Robespierre   |   PBS Extended Interviews > Resources > For Educators
    > Mad Max: Maximilien Robespierre Maximilien Robespierre was born May 6, 1758
    in Arras, France. Educated at the Lycée Louis-le-Grand in Paris as a lawyer, Robespierre
    became a disciple of philosopher Jean-Jacques Rousseau and a passionate advocate
    for the poor. Called "the Incorruptible" because of his unwavering dedication
    to the Revolution, Robespierre joined the Jacobin Club and earned a loyal following.
    In contrast to the more republican Girondins and Marie Antoinette, Robespierre
    fiercely opposed declaring war on Austria, feeling it would distract from revolutionary
    progress in France. Robespierre''s exemplary oratory skills influenced the National
    Convention in 1792 to avoid seeking public opinion about the Convention’s decision
    to execute King Louis XVI.  In 1793, the Convention elected Robespierre to the
    Committee of Public Defense. He was a highly controversial member, developing
    radical policies, warning of conspiracies, and suggesting restructuring the Convention.
    This behavior eventually led to his downfall, and he was guillotined without trial
    on 10th Thermidor An II (July 28, 1794), marking the end of the Reign of Terror.
    Famous Faces'
  - Devices for Arrhythmia Devices for Arrhythmia Updated:Dec 21,2016 In a medical
    emergency, life-threatening arrhythmias may be stopped by giving the heart an
    electric shock (as with a defibrillator ). For people with recurrent arrhythmias,
    medical devices such as a pacemaker and implantable cardioverter defibrillator
    (ICD) can help by continuously monitoring the heart's electrical system and providing
    automatic correction when an arrhythmia starts to occur. This section covers everything
    you need to know about these devices. Implantable Cardioverter Defibrillator (ICD)
  - 'vintage cleats | eBay vintage cleats: 1 2 3 4 5 eBay determines this price through
    a machine learned model of the product''s sale prices within the last 90 days.
    eBay determines trending price through a machine learned model of the product’s
    sale prices within the last 90 days. "New" refers to a brand-new, unused, unopened,
    undamaged item, and "Used" refers to an item that has been used previously. Top
    Rated Plus Sellers with highest buyer ratings Returns, money back Sellers with
    highest buyer ratings Returns, money back'
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.8253431554642914
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.870857890879963
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8653068915625914
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8667110599943904
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8671346646296434
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8681442638917114
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7826717704847901
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7685403521338614
      name: Spearman Dot
    - type: pearson_max
      value: 0.8671346646296434
      name: Pearson Max
    - type: spearman_max
      value: 0.870857890879963
      name: Spearman Max
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: allNLI dev
      type: allNLI-dev
    metrics:
    - type: cosine_accuracy
      value: 0.71875
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.8745474815368652
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.617169373549884
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7519949674606323
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.5155038759689923
      name: Cosine Precision
    - type: cosine_recall
      value: 0.7687861271676301
      name: Cosine Recall
    - type: cosine_ap
      value: 0.6116004689391709
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.693359375
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 401.3755187988281
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.566735112936345
      name: Dot F1
    - type: dot_f1_threshold
      value: 295.2575988769531
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.4394904458598726
      name: Dot Precision
    - type: dot_recall
      value: 0.7976878612716763
      name: Dot Recall
    - type: dot_ap
      value: 0.5243551756921989
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.724609375
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 228.3092498779297
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.6267281105990783
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 266.0207824707031
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.5210727969348659
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.7861271676300579
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.6101425904568746
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.720703125
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 9.726119041442871
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.6303854875283447
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 14.837699890136719
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.5186567164179104
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.8034682080924855
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.6172110045723997
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.724609375
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 401.3755187988281
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6303854875283447
      name: Max F1
    - type: max_f1_threshold
      value: 295.2575988769531
      name: Max F1 Threshold
    - type: max_precision
      value: 0.5210727969348659
      name: Max Precision
    - type: max_recall
      value: 0.8034682080924855
      name: Max Recall
    - type: max_ap
      value: 0.6172110045723997
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Qnli dev
      type: Qnli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.673828125
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7472400069236755
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.6863468634686347
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7334084510803223
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.6078431372549019
      name: Cosine Precision
    - type: cosine_recall
      value: 0.788135593220339
      name: Cosine Recall
    - type: cosine_ap
      value: 0.7293502303398447
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.6484375
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 392.88726806640625
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.6634920634920635
      name: Dot F1
    - type: dot_f1_threshold
      value: 310.97833251953125
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.5304568527918782
      name: Dot Precision
    - type: dot_recall
      value: 0.885593220338983
      name: Dot Recall
    - type: dot_ap
      value: 0.6331200610041253
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.671875
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 277.69342041015625
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.6830122591943958
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 301.36639404296875
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.582089552238806
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.826271186440678
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.7276384343706648
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.68359375
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 15.343950271606445
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.6895238095238095
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 15.738676071166992
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.6262975778546713
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.7669491525423728
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.7307379367367225
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.68359375
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 392.88726806640625
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.6895238095238095
      name: Max F1
    - type: max_f1_threshold
      value: 310.97833251953125
      name: Max F1 Threshold
    - type: max_precision
      value: 0.6262975778546713
      name: Max Precision
    - type: max_recall
      value: 0.885593220338983
      name: Max Recall
    - type: max_ap
      value: 0.7307379367367225
      name: Max Ap
---

# SentenceTransformer based on microsoft/deberta-v3-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the negation-triplets, [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), xsum-pairs, [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq), [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) and global_dataset datasets. 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-base](https://huggingface.co/microsoft/deberta-v3-base) <!-- at revision 8ccc9b6f36199bec6961081d44eb72fb3f7353f3 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - negation-triplets
    - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc)
    - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail)
    - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail)
    - xsum-pairs
    - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq)
    - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc)
    - openbookqa_pairs
    - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3)
    - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions)
    - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa)
    - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq)
    - [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws)
    - global_dataset
- **Language:** en
<!-- - **License:** Unknown -->
## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8253     |
| **spearman_cosine** | **0.8709** |
| pearson_manhattan   | 0.8653     |
| spearman_manhattan  | 0.8667     |
| pearson_euclidean   | 0.8671     |
| spearman_euclidean  | 0.8681     |
| pearson_dot         | 0.7827     |
| spearman_dot        | 0.7685     |
| pearson_max         | 0.8671     |
| spearman_max        | 0.8709     |


<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->


### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 96
- `per_device_eval_batch_size`: 68
- `learning_rate`: 3.5e-05
- `weight_decay`: 0.0005
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 3.5, 'min_lr': 1.5e-05}
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa3-base-STr-CosineWaves-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 96
- `per_device_eval_batch_size`: 68
- `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`: 3.5e-05
- `weight_decay`: 0.0005
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 3.5, 'min_lr': 1.5e-05}
- `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-base-STr-CosineWaves-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

</details>


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0
- Accelerate: 0.33.0
- Datasets: 2.21.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```