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---
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",
}
``` |