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:
- 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
Metric | Value |
---|---|
cosine_accuracy | 0.6653 |
cosine_accuracy_threshold | 0.6692 |
cosine_f1 | 0.7051 |
cosine_f1_threshold | 0.5758 |
cosine_precision | 0.5903 |
cosine_recall | 0.8753 |
cosine_ap | 0.7024 |
dot_accuracy | 0.6308 |
dot_accuracy_threshold | 127.0527 |
dot_f1 | 0.6984 |
dot_f1_threshold | 101.7725 |
dot_precision | 0.5773 |
dot_recall | 0.8838 |
dot_ap | 0.6558 |
manhattan_accuracy | 0.6675 |
manhattan_accuracy_threshold | 210.9939 |
manhattan_f1 | 0.7108 |
manhattan_f1_threshold | 252.6531 |
manhattan_precision | 0.6061 |
manhattan_recall | 0.8592 |
manhattan_ap | 0.7094 |
euclidean_accuracy | 0.6619 |
euclidean_accuracy_threshold | 11.2276 |
euclidean_f1 | 0.7073 |
euclidean_f1_threshold | 12.8508 |
euclidean_precision | 0.5879 |
euclidean_recall | 0.8876 |
euclidean_ap | 0.7038 |
max_accuracy | 0.6675 |
max_accuracy_threshold | 210.9939 |
max_f1 | 0.7108 |
max_f1_threshold | 252.6531 |
max_precision | 0.6061 |
max_recall | 0.8876 |
max_ap | 0.7094 |
Training Details
Training Dataset
stanfordnlp/snli
- Dataset: stanfordnlp/snli at cdb5c3d
- Size: 67,190 training samples
- Columns:
sentence1
,sentence2
, andlabel
- 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
, andlabel
- 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
: stepsper_device_train_batch_size
: 45per_device_eval_batch_size
: 22learning_rate
: 3e-06weight_decay
: 1e-09num_train_epochs
: 2lr_scheduler_type
: cosinewarmup_ratio
: 0.5save_safetensors
: Falsefp16
: Truepush_to_hub
: Truehub_model_id
: bobox/DeBERTaV3-small-ST-AdaptiveLayer-3L-ep2-nhub_strategy
: checkpointbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 45per_device_eval_batch_size
: 22per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 3e-06weight_decay
: 1e-09adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 2max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.5warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Falsesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Trueresume_from_checkpoint
: Nonehub_model_id
: bobox/DeBERTaV3-small-ST-AdaptiveLayer-3L-ep2-nhub_strategy
: checkpointhub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | loss | max_ap |
---|---|---|---|---|
0.1004 | 150 | 4.9809 | - | - |
0.2001 | 299 | - | 3.8956 | 0.6130 |
0.2008 | 300 | 3.8459 | - | - |
0.3012 | 450 | 3.1941 | - | - |
0.4003 | 598 | - | 3.2066 | 0.6526 |
0.4016 | 600 | 2.7939 | - | - |
0.5020 | 750 | 2.3082 | - | - |
0.6004 | 897 | - | 2.4595 | 0.6884 |
0.6024 | 900 | 1.9658 | - | - |
0.7028 | 1050 | 1.6975 | - | - |
0.8005 | 1196 | - | 2.0292 | 0.7010 |
0.8032 | 1200 | 1.528 | - | - |
0.9036 | 1350 | 1.3763 | - | - |
1.0007 | 1495 | - | 1.8192 | 0.7071 |
1.0040 | 1500 | 1.262 | - | - |
1.1044 | 1650 | 1.2033 | - | - |
1.2008 | 1794 | - | 1.6673 | 0.7082 |
1.2048 | 1800 | 1.1221 | - | - |
1.3052 | 1950 | 1.0963 | - | - |
1.4009 | 2093 | - | 1.5816 | 0.7103 |
1.4056 | 2100 | 1.0742 | - | - |
1.5060 | 2250 | 1.0242 | - | - |
1.6011 | 2392 | - | 1.5368 | 0.7094 |
1.6064 | 2400 | 1.0036 | - | - |
1.7068 | 2550 | 1.0143 | - | - |
1.8012 | 2691 | - | 1.5158 | 0.7094 |
1.8072 | 2700 | 0.9799 | - | - |
1.9076 | 2850 | 0.9777 | - | - |
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}
}
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Model tree for bobox/DeBERTaV3-small-ST-AdaptiveLayer-3L-ep2
Base model
microsoft/deberta-v3-smallDataset used to train bobox/DeBERTaV3-small-ST-AdaptiveLayer-3L-ep2
Evaluation results
- Cosine Accuracy on Unknownself-reported0.665
- Cosine Accuracy Threshold on Unknownself-reported0.669
- Cosine F1 on Unknownself-reported0.705
- Cosine F1 Threshold on Unknownself-reported0.576
- Cosine Precision on Unknownself-reported0.590
- Cosine Recall on Unknownself-reported0.875
- Cosine Ap on Unknownself-reported0.702
- Dot Accuracy on Unknownself-reported0.631
- Dot Accuracy Threshold on Unknownself-reported127.053
- Dot F1 on Unknownself-reported0.698