bobox / DeBERTaV3-small-ST-AdaptiveLayer-3L-ep2

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from microsoft/deberta-v3-small on the 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Dataset:
  • stanfordnlp/snli
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTaV3-small-ST-AdaptiveLayer-3L-ep2")
# Run inference
sentences = [
    'These girls are having a great time looking for seashells.',
    'The girls are happy.',
    'A girl is standing outside.',
]
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

Binary Classification

• Evaluated with BinaryClassificationEvaluator

Training Details

Training Dataset

stanfordnlp/snli

• Dataset: stanfordnlp/snli at cdb5c3d
• Size: 67,190 training samples
• Columns: sentence1 , sentence2 , and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 4 tokens mean: 21.19 tokens max: 133 tokens	min: 4 tokens mean: 11.77 tokens max: 49 tokens	0: 100.00%

• Samples:

  sentence1	sentence2	label
  Without a placebo group, we still won't know if any of the treatments are better than nothing and therefore worth giving.	It is necessary to use a controlled method to ensure the treatments are worthwhile.	0
  It was conducted in silence.	It was done silently.	0
  oh Lewisville any decent food in your cafeteria up there	Is there any decent food in your cafeteria up there in Lewisville?	0

• Loss: AdaptiveLayerLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 3,
      "last_layer_weight": 1,
      "prior_layers_weight": 0.3,
      "kl_div_weight": 1,
      "kl_temperature": 1
  }
  

Evaluation Dataset

stanfordnlp/snli

• Dataset: stanfordnlp/snli at cdb5c3d
• Size: 6,626 evaluation samples
• Columns: premise , hypothesis , and label
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	int
  details	min: 6 tokens mean: 17.28 tokens max: 59 tokens	min: 4 tokens mean: 10.53 tokens max: 32 tokens	0: ~48.70% 1: ~51.30%

• Samples:

  premise	hypothesis	label
  This church choir sings to the masses as they sing joyous songs from the book at a church.	The church has cracks in the ceiling.	0
  This church choir sings to the masses as they sing joyous songs from the book at a church.	The church is filled with song.	1
  A woman with a green headscarf, blue shirt and a very big grin.	The woman is young.	0

• Loss: AdaptiveLayerLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "n_layers_per_step": 3,
      "last_layer_weight": 1,
      "prior_layers_weight": 0.3,
      "kl_div_weight": 1,
      "kl_temperature": 1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 45
• per_device_eval_batch_size : 22
• learning_rate : 3e-06
• weight_decay : 1e-09
• num_train_epochs : 2
• lr_scheduler_type : cosine
• warmup_ratio : 0.5
• save_safetensors : False
• fp16 : True
• push_to_hub : True
• hub_model_id : bobox/DeBERTaV3-small-ST-AdaptiveLayer-3L-ep2-n
• hub_strategy : checkpoint
• batch_sampler : no_duplicates

All Hyperparameters

Training Logs

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

@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

@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

@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}
}