--- 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 - sentence-transformers/stsb metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - 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: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.7641416788909702 name: Pearson Cosine - type: spearman_cosine value: 0.763668633314844 name: Spearman Cosine - type: pearson_manhattan value: 0.7808845626705342 name: Pearson Manhattan - type: spearman_manhattan value: 0.783960481366303 name: Spearman Manhattan - type: pearson_euclidean value: 0.7714319160162553 name: Pearson Euclidean - type: spearman_euclidean value: 0.7750607015673249 name: Spearman Euclidean - type: pearson_dot value: 0.587659176024498 name: Pearson Dot - type: spearman_dot value: 0.6010467058509925 name: Spearman Dot - type: pearson_max value: 0.7808845626705342 name: Pearson Max - type: spearman_max value: 0.783960481366303 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.6773826673743271 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.5830236673355103 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.7209834880077135 name: Cosine F1 - type: cosine_f1_threshold value: 0.5085207223892212 name: Cosine F1 Threshold - type: cosine_precision value: 0.6137273007079102 name: Cosine Precision - type: cosine_recall value: 0.873667299547247 name: Cosine Recall - type: cosine_ap value: 0.7219177301725319 name: Cosine Ap - type: dot_accuracy value: 0.6389415421942528 name: Dot Accuracy - type: dot_accuracy_threshold value: 45.1016845703125 name: Dot Accuracy Threshold - type: dot_f1 value: 0.7090406632451638 name: Dot F1 - type: dot_f1_threshold value: 32.459449768066406 name: Dot F1 Threshold - type: dot_precision value: 0.5775450202131569 name: Dot Precision - type: dot_recall value: 0.9180663064115671 name: Dot Recall - type: dot_ap value: 0.6795197111227502 name: Dot Ap - type: manhattan_accuracy value: 0.6625217984684206 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 158.29489135742188 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.7041269465332466 name: Manhattan F1 - type: manhattan_f1_threshold value: 178.5047607421875 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5921131248755228 name: Manhattan Precision - type: manhattan_recall value: 0.8684095224185775 name: Manhattan Recall - type: manhattan_ap value: 0.7054112942825768 name: Manhattan Ap - type: euclidean_accuracy value: 0.6578967321252559 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 7.951424598693848 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.7015471831817645 name: Euclidean F1 - type: euclidean_f1_threshold value: 9.045232772827148 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5888767720828789 name: Euclidean Precision - type: euclidean_recall value: 0.8675332262304659 name: Euclidean Recall - type: euclidean_ap value: 0.7024193897121154 name: Euclidean Ap - type: max_accuracy value: 0.6773826673743271 name: Max Accuracy - type: max_accuracy_threshold value: 158.29489135742188 name: Max Accuracy Threshold - type: max_f1 value: 0.7209834880077135 name: Max F1 - type: max_f1_threshold value: 178.5047607421875 name: Max F1 Threshold - type: max_precision value: 0.6137273007079102 name: Max Precision - type: max_recall value: 0.9180663064115671 name: Max Recall - type: max_ap value: 0.7219177301725319 name: Max Ap --- # 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-SentenceTransformer-AdaptiveLayerAll") # 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] ``` ## Evaluation ### Metrics #### Semantic Similarity * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7641 | | **spearman_cosine** | **0.7637** | | pearson_manhattan | 0.7809 | | spearman_manhattan | 0.784 | | pearson_euclidean | 0.7714 | | spearman_euclidean | 0.7751 | | pearson_dot | 0.5877 | | spearman_dot | 0.601 | | pearson_max | 0.7809 | | spearman_max | 0.784 | #### Binary Classification * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6774 | | cosine_accuracy_threshold | 0.583 | | cosine_f1 | 0.721 | | cosine_f1_threshold | 0.5085 | | cosine_precision | 0.6137 | | cosine_recall | 0.8737 | | cosine_ap | 0.7219 | | dot_accuracy | 0.6389 | | dot_accuracy_threshold | 45.1017 | | dot_f1 | 0.709 | | dot_f1_threshold | 32.4594 | | dot_precision | 0.5775 | | dot_recall | 0.9181 | | dot_ap | 0.6795 | | manhattan_accuracy | 0.6625 | | manhattan_accuracy_threshold | 158.2949 | | manhattan_f1 | 0.7041 | | manhattan_f1_threshold | 178.5048 | | manhattan_precision | 0.5921 | | manhattan_recall | 0.8684 | | manhattan_ap | 0.7054 | | euclidean_accuracy | 0.6579 | | euclidean_accuracy_threshold | 7.9514 | | euclidean_f1 | 0.7015 | | euclidean_f1_threshold | 9.0452 | | euclidean_precision | 0.5889 | | euclidean_recall | 0.8675 | | euclidean_ap | 0.7024 | | max_accuracy | 0.6774 | | max_accuracy_threshold | 158.2949 | | max_f1 | 0.721 | | max_f1_threshold | 178.5048 | | max_precision | 0.6137 | | max_recall | 0.9181 | | **max_ap** | **0.7219** | ## 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: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | 0 | | Children smiling and waving at camera | There are children present | 0 | | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | 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": 1.2, "kl_temperature": 1.2 } ``` ### Evaluation Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * 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": 1.2, "kl_temperature": 1.2 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `learning_rate`: 5e-06 - `weight_decay`: 1e-07 - `warmup_ratio`: 0.33 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAlln - `hub_strategy`: checkpoint - `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`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-06 - `weight_decay`: 1e-07 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.33 - `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`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAlln - `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
### Training Logs | Epoch | Step | Training Loss | loss | max_ap | spearman_cosine | |:------:|:-----:|:-------------:|:------:|:------:|:---------------:| | None | 0 | - | 5.4171 | - | 0.4276 | | 0.1501 | 1474 | 4.9879 | - | - | - | | 0.3000 | 2947 | - | 2.6463 | 0.6840 | - | | 0.3001 | 2948 | 3.2669 | - | - | - | | 0.4502 | 4422 | 2.6363 | - | - | - | | 0.6000 | 5894 | - | 1.8436 | 0.7014 | - | | 0.6002 | 5896 | 2.192 | - | - | - | | 0.7503 | 7370 | 0.8208 | - | - | - | | 0.9000 | 8841 | - | 1.5551 | 0.7065 | - | | 0.9003 | 8844 | 0.6161 | - | - | - | | 1.0504 | 10318 | 1.0301 | - | - | - | | 1.2000 | 11788 | - | 1.1883 | 0.7131 | - | | 1.2004 | 11792 | 1.8209 | - | - | - | | 1.3505 | 13266 | 1.6887 | - | - | - | | 1.5001 | 14735 | - | 1.1067 | 0.7119 | - | | 1.5006 | 14740 | 1.6114 | - | - | - | | 1.6506 | 16214 | 1.0691 | - | - | - | | 1.8001 | 17682 | - | 1.0872 | 0.7183 | - | | 1.8007 | 17688 | 0.3982 | - | - | - | | 1.9507 | 19162 | 0.3659 | - | - | - | | 2.1001 | 20629 | - | 0.9642 | 0.7221 | - | | 2.1008 | 20636 | 1.1702 | - | - | - | | 2.2508 | 22110 | 1.4984 | - | - | - | | 2.4001 | 23576 | - | 0.9437 | 0.7200 | - | | 2.4009 | 23584 | 1.4609 | - | - | - | | 2.5510 | 25058 | 1.4477 | - | - | - | | 2.7001 | 26523 | - | 0.9428 | 0.7216 | - | | 2.7010 | 26532 | 0.5802 | - | - | - | | 2.8511 | 28006 | 0.3297 | - | - | - | | 3.0 | 29469 | - | 0.9532 | 0.7219 | - | | None | 0 | - | 2.4079 | 0.7219 | 0.7637 | ### 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} } ```