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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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	<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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