bobox/DeBERTaV3-small-ST-AdaptiveLayerAllNormalized

SentenceTransformer based on microsoft/deberta-v3-small

[nlayersperstep = -1, lastlayerweight = 1 * (modellayers-1), priorlayersweight= 0.85, kldivweight = 2, kl_temperature= 10, lr = 1e-6. batch = 42, schedule = cosine]

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 <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —stanfordnlp/snli
• —Language: en <!-- - License: Unknown -->

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-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]

<!--

Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details> -->

<!--

Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details> -->

<!--

Out-of-Scope Use

List how the model may foreseeably be misused and address what users ought not to do with the model. -->

Evaluation

Metrics

Binary Classification

• —Evaluated with <code>BinaryClassificationEvaluator</code>

<!--

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. -->

<!--

Recommendations

What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -->

Training Details

Training Dataset

stanfordnlp/snli

• —Dataset: stanfordnlp/snli at cdb5c3d
• —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> with these parameters:

  {
      "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 at cdb5c3d
• —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> with these parameters:

  {
      "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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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

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}
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->