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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:314315
- loss:AdaptiveLayerLoss
- loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-small
datasets:
- stanfordnlp/snli
metrics:
- 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
widget:
- source_sentence: The pitcher is pitching the ball in a game of baseball.
sentences:
- the lady digs into the ground
- A group of people are sitting at tables.
- The pitcher throws the ball.
- source_sentence: People are conversing at a dining table under a canopy.
sentences:
- A canine is using his legs.
- The people are creative.
- People at a party are seated for dinner on the lawn.
- source_sentence: Two teenage girls conversing next to lockers.
sentences:
- Girls talking about their problems next to lockers.
- A group of people play in the ocean.
- The man is testing the bike.
- source_sentence: A young boy in a hoodie climbs a red slide sitting on a red and
green checkered background.
sentences:
- People are buying food from a street vendor.
- A boy is playing.
- A dog outside digging.
- source_sentence: A professional swimmer spits water out after surfacing while grabbing
the hand of someone helping him back to land.
sentences:
- A group of people wait in a line.
- A tourist has his picture taken on Easter Island.
- The swimmer almost drowned after being sucked under a fast current.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.6578209113655319
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7228835821151733
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7058138858173776
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6018929481506348
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.586687306501548
name: Cosine Precision
- type: cosine_recall
value: 0.8856433474514386
name: Cosine Recall
- type: cosine_ap
value: 0.6972177912771047
name: Cosine Ap
- type: dot_accuracy
value: 0.6157403897187049
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 240.6935577392578
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6994949494949494
name: Dot F1
- type: dot_f1_threshold
value: 180.59024047851562
name: Dot F1 Threshold
- type: dot_precision
value: 0.5603834989884774
name: Dot Precision
- type: dot_recall
value: 0.9304805024098145
name: Dot Recall
- type: dot_ap
value: 0.6228322985998769
name: Dot Ap
- type: manhattan_accuracy
value: 0.6658579118962772
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 281.63262939453125
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7096774193548386
name: Manhattan F1
- type: manhattan_f1_threshold
value: 315.9024658203125
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.6168446026097272
name: Manhattan Precision
- type: manhattan_recall
value: 0.8354023659997079
name: Manhattan Recall
- type: manhattan_ap
value: 0.7109579985461502
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.6626734399878687
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 14.194840431213379
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7064288581751448
name: Euclidean F1
- type: euclidean_f1_threshold
value: 17.004133224487305
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.581586402266289
name: Euclidean Precision
- type: euclidean_recall
value: 0.8995180370965387
name: Euclidean Recall
- type: euclidean_ap
value: 0.7094433163219231
name: Euclidean Ap
- type: max_accuracy
value: 0.6658579118962772
name: Max Accuracy
- type: max_accuracy_threshold
value: 281.63262939453125
name: Max Accuracy Threshold
- type: max_f1
value: 0.7096774193548386
name: Max F1
- type: max_f1_threshold
value: 315.9024658203125
name: Max F1 Threshold
- type: max_precision
value: 0.6168446026097272
name: Max Precision
- type: max_recall
value: 0.9304805024098145
name: Max Recall
- type: max_ap
value: 0.7109579985461502
name: Max Ap
---
# SentenceTransformer based on microsoft/deberta-v3-small
[n_layers_per_step = -1, last_layer_weight = 1 * (model_layers-1), prior_layers_weight= 0.85, kl_div_weight = 2, kl_temperature= 10, lr = 1e-6. batch = 42, schedule = cosine]
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) dataset. 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) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli)
- **Language:** en
<!-- - **License:** Unknown -->
### 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): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': 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/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm")
# Run inference
sentences = [
'A professional swimmer spits water out after surfacing while grabbing the hand of someone helping him back to land.',
'The swimmer almost drowned after being sucked under a fast current.',
'A group of people wait in a line.',
]
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]
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Binary Classification
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:----------|
| cosine_accuracy | 0.6578 |
| cosine_accuracy_threshold | 0.7229 |
| cosine_f1 | 0.7058 |
| cosine_f1_threshold | 0.6019 |
| cosine_precision | 0.5867 |
| cosine_recall | 0.8856 |
| cosine_ap | 0.6972 |
| dot_accuracy | 0.6157 |
| dot_accuracy_threshold | 240.6936 |
| dot_f1 | 0.6995 |
| dot_f1_threshold | 180.5902 |
| dot_precision | 0.5604 |
| dot_recall | 0.9305 |
| dot_ap | 0.6228 |
| manhattan_accuracy | 0.6659 |
| manhattan_accuracy_threshold | 281.6326 |
| manhattan_f1 | 0.7097 |
| manhattan_f1_threshold | 315.9025 |
| manhattan_precision | 0.6168 |
| manhattan_recall | 0.8354 |
| manhattan_ap | 0.711 |
| euclidean_accuracy | 0.6627 |
| euclidean_accuracy_threshold | 14.1948 |
| euclidean_f1 | 0.7064 |
| euclidean_f1_threshold | 17.0041 |
| euclidean_precision | 0.5816 |
| euclidean_recall | 0.8995 |
| euclidean_ap | 0.7094 |
| max_accuracy | 0.6659 |
| max_accuracy_threshold | 281.6326 |
| max_f1 | 0.7097 |
| max_f1_threshold | 315.9025 |
| max_precision | 0.6168 |
| max_recall | 0.9305 |
| **max_ap** | **0.711** |
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## Training Details
### Training Dataset
#### stanfordnlp/snli
* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 314,315 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>0</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"n_layers_per_step": -1,
"last_layer_weight": 6,
"prior_layers_weight": 0.85,
"kl_div_weight": 2,
"kl_temperature": 10
}
```
### Evaluation Dataset
#### stanfordnlp/snli
* Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 13,189 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.28 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.53 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>0: ~48.70%</li><li>1: ~51.30%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------|:---------------|
| <code>This church choir sings to the masses as they sing joyous songs from the book at a church.</code> | <code>The church has cracks in the ceiling.</code> | <code>0</code> |
| <code>This church choir sings to the masses as they sing joyous songs from the book at a church.</code> | <code>The church is filled with song.</code> | <code>1</code> |
| <code>A woman with a green headscarf, blue shirt and a very big grin.</code> | <code>The woman is young.</code> | <code>0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"n_layers_per_step": -1,
"last_layer_weight": 6,
"prior_layers_weight": 0.85,
"kl_div_weight": 2,
"kl_temperature": 10
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 42
- `per_device_eval_batch_size`: 32
- `learning_rate`: 1e-06
- `weight_decay`: 1e-08
- `num_train_epochs`: 1
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `save_safetensors`: False
- `fp16`: True
- `hub_model_id`: bobox/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm-tmp
- `hub_strategy`: checkpoint
- `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`: 42
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 1e-06
- `weight_decay`: 1e-08
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm-tmp
- `hub_strategy`: checkpoint
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | max_ap |
|:------:|:----:|:-------------:|:-------:|:------:|
| 0.0501 | 375 | 23.8735 | 21.0352 | 0.6131 |
| 0.1002 | 750 | 22.4091 | 19.6992 | 0.6353 |
| 0.1503 | 1125 | 19.4663 | 16.2104 | 0.6580 |
| 0.2004 | 1500 | 15.348 | 13.2038 | 0.6732 |
| 0.2505 | 1875 | 12.5377 | 11.6357 | 0.6815 |
| 0.3006 | 2250 | 11.4576 | 10.7570 | 0.6862 |
| 0.3507 | 2625 | 10.7446 | 10.1819 | 0.6891 |
| 0.4009 | 3000 | 10.2323 | 9.7470 | 0.6904 |
| 0.4510 | 3375 | 9.9825 | 9.4256 | 0.6914 |
| 0.5011 | 3750 | 9.6954 | 9.2200 | 0.6923 |
| 0.5512 | 4125 | 9.6359 | 9.0367 | 0.6923 |
| 0.6013 | 4500 | 8.3103 | 7.8258 | 0.7026 |
| 0.6514 | 4875 | 4.4845 | 7.4044 | 0.7073 |
| 0.7015 | 5250 | 3.8303 | 7.2647 | 0.7092 |
| 0.7516 | 5625 | 3.5617 | 7.2020 | 0.7098 |
| 0.8017 | 6000 | 3.4088 | 7.1684 | 0.7103 |
| 0.8518 | 6375 | 3.347 | 7.1531 | 0.7108 |
| 0.9019 | 6750 | 3.2064 | 7.1451 | 0.7109 |
| 0.9520 | 7125 | 3.3096 | 7.1427 | 0.7110 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- Datasets: 2.19.2
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### AdaptiveLayerLoss
```bibtex
@misc{li20242d,
title={2D Matryoshka Sentence Embeddings},
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
year={2024},
eprint={2402.14776},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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