--- language: - en library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:67190 - 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 - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: A worker peers out from atop a building under construction. sentences: - The man pleads for mercy. - People and a baby crossing the street. - A person is atop of a building. - source_sentence: An aisle at Best Buy with an employee standing at the computer and a Geek Squad sign in the background. sentences: - the man is watching the stars - The employee is wearing a blue shirt. - A person balancing. - source_sentence: A man with a long white beard is examining a camera and another man with a black shirt is in the background. sentences: - a man is with another man - Children in uniforms climb a tower. - There are five children. - source_sentence: A black dog with a blue collar is jumping into the water. sentences: - The dog is playing tug of war with a stick. - There is a woman painting. - A black dog wearing a blue collar is chasing something into the water. - source_sentence: A wet child stands in chest deep ocean water. sentences: - A woman paints a portrait of her best friend. - A person in red is cutting the grass on a riding mower - The child s playing on the beach. 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: 0.6583157259281618 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.6766541004180908 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.7049362860324137 name: Cosine F1 - type: cosine_f1_threshold value: 0.6017583012580872 name: Cosine F1 Threshold - type: cosine_precision value: 0.6115046147241897 name: Cosine Precision - type: cosine_recall value: 0.8320677570093458 name: Cosine Recall - type: cosine_ap value: 0.6995030811464378 name: Cosine Ap - type: dot_accuracy value: 0.6272260790824027 name: Dot Accuracy - type: dot_accuracy_threshold value: 163.25054931640625 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6976381461675579 name: Dot F1 - type: dot_f1_threshold value: 119.20779418945312 name: Dot F1 Threshold - type: dot_precision value: 0.5639409221902018 name: Dot Precision - type: dot_recall value: 0.914427570093458 name: Dot Recall - type: dot_ap value: 0.643747511442345 name: Dot Ap - type: manhattan_accuracy value: 0.6571083610021129 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 243.75453186035156 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.7055783910745744 name: Manhattan F1 - type: manhattan_f1_threshold value: 295.95947265625 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5900608917697898 name: Manhattan Precision - type: manhattan_recall value: 0.8773364485981309 name: Manhattan Recall - type: manhattan_ap value: 0.7072033306346501 name: Manhattan Ap - type: euclidean_accuracy value: 0.6590703290069424 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 12.141830444335938 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.7036813518406759 name: Euclidean F1 - type: euclidean_f1_threshold value: 14.197540283203125 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5996708496194199 name: Euclidean Precision - type: euclidean_recall value: 0.8513434579439252 name: Euclidean Recall - type: euclidean_ap value: 0.7035256676322055 name: Euclidean Ap - type: max_accuracy value: 0.6590703290069424 name: Max Accuracy - type: max_accuracy_threshold value: 243.75453186035156 name: Max Accuracy Threshold - type: max_f1 value: 0.7055783910745744 name: Max F1 - type: max_f1_threshold value: 295.95947265625 name: Max F1 Threshold - type: max_precision value: 0.6115046147241897 name: Max Precision - type: max_recall value: 0.914427570093458 name: Max Recall - type: max_ap value: 0.7072033306346501 name: Max Ap - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.732169941341086 name: Pearson Cosine - type: spearman_cosine value: 0.7344587206087978 name: Spearman Cosine - type: pearson_manhattan value: 0.7537099624360986 name: Pearson Manhattan - type: spearman_manhattan value: 0.7550555196955944 name: Spearman Manhattan - type: pearson_euclidean value: 0.7468210439584286 name: Pearson Euclidean - type: spearman_euclidean value: 0.74849026008206 name: Spearman Euclidean - type: pearson_dot value: 0.6142835401925993 name: Pearson Dot - type: spearman_dot value: 0.6100201108417316 name: Spearman Dot - type: pearson_max value: 0.7537099624360986 name: Pearson Max - type: spearman_max value: 0.7550555196955944 name: Spearman Max --- # SentenceTransformer based on microsoft/deberta-v3-small 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) - **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 ### 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-ST-AdaptiveLayers-ep2") # Run inference sentences = [ 'A wet child stands in chest deep ocean water.', 'The child s playing on the beach.', 'A woman paints a portrait of her best friend.', ] 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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6583 | | cosine_accuracy_threshold | 0.6767 | | cosine_f1 | 0.7049 | | cosine_f1_threshold | 0.6018 | | cosine_precision | 0.6115 | | cosine_recall | 0.8321 | | cosine_ap | 0.6995 | | dot_accuracy | 0.6272 | | dot_accuracy_threshold | 163.2505 | | dot_f1 | 0.6976 | | dot_f1_threshold | 119.2078 | | dot_precision | 0.5639 | | dot_recall | 0.9144 | | dot_ap | 0.6437 | | manhattan_accuracy | 0.6571 | | manhattan_accuracy_threshold | 243.7545 | | manhattan_f1 | 0.7056 | | manhattan_f1_threshold | 295.9595 | | manhattan_precision | 0.5901 | | manhattan_recall | 0.8773 | | manhattan_ap | 0.7072 | | euclidean_accuracy | 0.6591 | | euclidean_accuracy_threshold | 12.1418 | | euclidean_f1 | 0.7037 | | euclidean_f1_threshold | 14.1975 | | euclidean_precision | 0.5997 | | euclidean_recall | 0.8513 | | euclidean_ap | 0.7035 | | max_accuracy | 0.6591 | | max_accuracy_threshold | 243.7545 | | max_f1 | 0.7056 | | max_f1_threshold | 295.9595 | | max_precision | 0.6115 | | max_recall | 0.9144 | | **max_ap** | **0.7072** | #### Semantic Similarity * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7322 | | **spearman_cosine** | **0.7345** | | pearson_manhattan | 0.7537 | | spearman_manhattan | 0.7551 | | pearson_euclidean | 0.7468 | | spearman_euclidean | 0.7485 | | pearson_dot | 0.6143 | | spearman_dot | 0.61 | | pearson_max | 0.7537 | | spearman_max | 0.7551 | ## 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: 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 | | | | * 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](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": 1, "prior_layers_weight": 1, "kl_div_weight": 1, "kl_temperature": 1 } ``` ### Evaluation Dataset #### stanfordnlp/snli * Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b) * Size: 1,500 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------|:------------------------------------------------------|:------------------| | A man with a hard hat is dancing. | A man wearing a hard hat is dancing. | 1.0 | | A young child is riding a horse. | A child is riding a horse. | 0.95 | | A man is feeding a mouse to a snake. | The man is feeding a mouse to the snake. | 1.0 | * Loss: [AdaptiveLayerLoss](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": 1, "prior_layers_weight": 1, "kl_div_weight": 1, "kl_temperature": 1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 42 - `per_device_eval_batch_size`: 22 - `learning_rate`: 3e-06 - `weight_decay`: 1e-08 - `num_train_epochs`: 2 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.5 - `save_safetensors`: False - `fp16`: True - `hub_model_id`: bobox/DeBERTaV3-small-ST-AdaptiveLayers-ep2-tmp - `hub_strategy`: checkpoint - `hub_private_repo`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `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`: 22 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 3e-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`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.5 - `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-ST-AdaptiveLayers-ep2-tmp - `hub_strategy`: checkpoint - `hub_private_repo`: True - `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
### Training Logs | Epoch | Step | Training Loss | loss | max_ap | spearman_cosine | |:-----:|:----:|:-------------:|:------:|:------:|:---------------:| | 0.1 | 160 | 4.6003 | 4.8299 | 0.6017 | - | | 0.2 | 320 | 4.0659 | 4.3436 | 0.6168 | - | | 0.3 | 480 | 3.4886 | 4.0840 | 0.6339 | - | | 0.4 | 640 | 3.0592 | 3.6422 | 0.6611 | - | | 0.5 | 800 | 2.5728 | 3.1927 | 0.6773 | - | | 0.6 | 960 | 2.184 | 2.8322 | 0.6893 | - | | 0.7 | 1120 | 1.8744 | 2.4892 | 0.6954 | - | | 0.8 | 1280 | 1.757 | 2.4453 | 0.7002 | - | | 0.9 | 1440 | 1.5872 | 2.2565 | 0.7010 | - | | 1.0 | 1600 | 1.446 | 2.1391 | 0.7046 | - | | 1.1 | 1760 | 1.3892 | 2.1236 | 0.7058 | - | | 1.2 | 1920 | 1.2567 | 1.9738 | 0.7053 | - | | 1.3 | 2080 | 1.2233 | 1.8925 | 0.7063 | - | | 1.4 | 2240 | 1.1954 | 1.8392 | 0.7075 | - | | 1.5 | 2400 | 1.1395 | 1.9081 | 0.7065 | - | | 1.6 | 2560 | 1.1211 | 1.8080 | 0.7074 | - | | 1.7 | 2720 | 1.0825 | 1.8408 | 0.7073 | - | | 1.8 | 2880 | 1.1358 | 1.7363 | 0.7073 | - | | 1.9 | 3040 | 1.0628 | 1.8936 | 0.7072 | - | | 2.0 | 3200 | 1.1412 | 1.7846 | 0.7072 | - | | None | 0 | - | 3.0121 | 0.7072 | 0.7345 | ### 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} } ```