--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:556850 - loss:ContradictionMarginLoss base_model: VinitT/Embeddings-Trivia widget: - source_sentence: Guy wearing sunglasses and blue shirt on skateboard in front of a bright yellow building with palm trees. sentences: - Two people are standing by the street. - A man rides a skateboard outside. - The boys are inside laying down. - source_sentence: In a park, a boy is bent to read the tree description, and a girl is standing nearby waiting for him. sentences: - A boy and girl out in the park while looking at the scenery. - Some girls are climbing. - An illiterate boy standing up reading a tree description. - source_sentence: A man in a blue shirt gesticulates as he speaks to a uniformed official. sentences: - A man has a mouthfull of meatballs. - A man is speaking with an official - Women are working in a lab - source_sentence: John left me, and a few minutes later I saw him from my window walking slowly across the grass arm in arm with Cynthia Murdoch. sentences: - John left me to then walk with Cynthia Murdoch. - A girl is wearing a crown while having a funny look on her face. - John stayed and ignored Cynthia as she walked by. - source_sentence: so he has overcome alcoholism at this point sentences: - A dog is holding a toy. - He still is a heavy drinker and can't control it. - He's gotten stronger and has overcome alcoholism. datasets: - sentence-transformers/all-nli pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on VinitT/Embeddings-Trivia results: - task: type: triplet name: Triplet dataset: name: contra eval type: contra_eval metrics: - type: cosine_accuracy value: 0.949999988079071 name: Cosine Accuracy --- # SentenceTransformer based on VinitT/Embeddings-Trivia This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [VinitT/Embeddings-Trivia](https://huggingface.co/VinitT/Embeddings-Trivia) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 384-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:** [VinitT/Embeddings-Trivia](https://huggingface.co/VinitT/Embeddings-Trivia) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 384, '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}) (2): Normalize() ) ``` ## 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("sentence_transformers_model_id") # Run inference sentences = [ 'so he has overcome alcoholism at this point', "He's gotten stronger and has overcome alcoholism.", "He still is a heavy drinker and can't control it.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.7603, 0.0849], # [0.7603, 1.0000, 0.0794], # [0.0849, 0.0794, 1.0000]]) ``` ## Evaluation ### Metrics #### Triplet * Dataset: `contra_eval` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:---------| | **cosine_accuracy** | **0.95** | ## Training Details ### Training Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 556,850 training samples * Columns: anchor, positive, negative, and label * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | string | int | | details | | | | | * Samples: | anchor | positive | negative | label | |:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------------------------------------------|:---------------| | a young girl wearing blue smiles. | A little girl wears blue. | A little girl frowns as she wears an ugly burlap sack. | 1 | | An old man wearing a tan jacket and blue pants standing on a sidewalk with a small suitcase. | A man wearing a jacket and jeans holds a suitcase. | A young woman sits on a bench holding her purse. | 1 | | The people are inside. | Two people are dancing by a red couch. | People walk up and down the steps in front of a church. | 1 | * Loss: custom_loss.ContradictionMarginLoss with these parameters: ```json { "margin_neutral": 0.2, "margin_contradiction": 0.4 } ``` ### Evaluation Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 1,000 evaluation samples * Columns: anchor, positive, negative, and label * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | string | int | | details | | | | | * Samples: | anchor | positive | negative | label | |:------------------------------------------------------|:---------------------------------------------------------------------------|:----------------------------------------------------|:---------------| | An older man riding a bike. | An elderly man is biking | an old man is sleeping | 1 | | The man is on a skateboard. | A shirtless man is doing a skateboard trick over a bike rail. | A man performs a bike trick on a ramp. | 1 | | The Episcopalians are all going to hell. | The Episcopalians will not be going to heaven. | All Episcopalians will go to heaven. | 1 | * Loss: custom_loss.ContradictionMarginLoss with these parameters: ```json { "margin_neutral": 0.2, "margin_contradiction": 0.4 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `warmup_steps`: 0.1 - `fp16`: True - `load_best_model_at_end`: True #### All Hyperparameters
Click to expand - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.01 - `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`: linear - `lr_scheduler_kwargs`: None - `warmup_ratio`: 0.1 - `warmup_steps`: 0.1 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `enable_jit_checkpoint`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `use_cpu`: False - `seed`: 42 - `data_seed`: None - `bf16`: False - `fp16`: True - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: -1 - `ddp_backend`: None - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `group_by_length`: False - `length_column_name`: length - `project`: huggingface - `trackio_space_id`: trackio - `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 - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `auto_find_batch_size`: False - `full_determinism`: False - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_num_input_tokens_seen`: no - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: True - `use_cache`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | contra_eval_cosine_accuracy | |:---------:|:--------:|:-------------:|:---------------:|:---------------------------:| | 0.0001 | 1 | 0.2363 | - | - | | 0.0057 | 50 | 0.1877 | - | - | | 0.0115 | 100 | 0.1786 | - | - | | 0.0172 | 150 | 0.1672 | - | - | | 0.0230 | 200 | 0.1529 | - | - | | 0.0287 | 250 | 0.1392 | - | - | | 0.0345 | 300 | 0.1278 | - | - | | 0.0402 | 350 | 0.1233 | - | - | | 0.0460 | 400 | 0.1157 | - | - | | 0.0517 | 450 | 0.1116 | - | - | | 0.0575 | 500 | 0.1063 | 0.0983 | 0.9260 | | 0.0632 | 550 | 0.1087 | - | - | | 0.0690 | 600 | 0.1016 | - | - | | 0.0747 | 650 | 0.1026 | - | - | | 0.0805 | 700 | 0.0967 | - | - | | 0.0862 | 750 | 0.0990 | - | - | | 0.0919 | 800 | 0.0925 | - | - | | 0.0977 | 850 | 0.0965 | - | - | | 0.1034 | 900 | 0.0981 | - | - | | 0.1092 | 950 | 0.0881 | - | - | | 0.1149 | 1000 | 0.0920 | 0.0829 | 0.9410 | | 0.1207 | 1050 | 0.0882 | - | - | | 0.1264 | 1100 | 0.0839 | - | - | | 0.1322 | 1150 | 0.0896 | - | - | | 0.1379 | 1200 | 0.0858 | - | - | | 0.1437 | 1250 | 0.0878 | - | - | | 0.1494 | 1300 | 0.0857 | - | - | | 0.1552 | 1350 | 0.0902 | - | - | | 0.1609 | 1400 | 0.0793 | - | - | | 0.1666 | 1450 | 0.0830 | - | - | | 0.1724 | 1500 | 0.0827 | 0.0788 | 0.9380 | | 0.1781 | 1550 | 0.0789 | - | - | | 0.1839 | 1600 | 0.0834 | - | - | | 0.1896 | 1650 | 0.0805 | - | - | | 0.1954 | 1700 | 0.0795 | - | - | | 0.2011 | 1750 | 0.0846 | - | - | | 0.2069 | 1800 | 0.0822 | - | - | | 0.2126 | 1850 | 0.0858 | - | - | | 0.2184 | 1900 | 0.0785 | - | - | | 0.2241 | 1950 | 0.0777 | - | - | | 0.2299 | 2000 | 0.0746 | 0.0721 | 0.9460 | | 0.2356 | 2050 | 0.0798 | - | - | | 0.2414 | 2100 | 0.0798 | - | - | | 0.2471 | 2150 | 0.0794 | - | - | | 0.2528 | 2200 | 0.0769 | - | - | | 0.2586 | 2250 | 0.0805 | - | - | | 0.2643 | 2300 | 0.0782 | - | - | | 0.2701 | 2350 | 0.0776 | - | - | | 0.2758 | 2400 | 0.0776 | - | - | | 0.2816 | 2450 | 0.0733 | - | - | | 0.2873 | 2500 | 0.0750 | 0.0718 | 0.9440 | | 0.2931 | 2550 | 0.0764 | - | - | | 0.2988 | 2600 | 0.0775 | - | - | | 0.3046 | 2650 | 0.0767 | - | - | | 0.3103 | 2700 | 0.0766 | - | - | | 0.3161 | 2750 | 0.0755 | - | - | | 0.3218 | 2800 | 0.0752 | - | - | | 0.3275 | 2850 | 0.0717 | - | - | | 0.3333 | 2900 | 0.0714 | - | - | | 0.3390 | 2950 | 0.0726 | - | - | | 0.3448 | 3000 | 0.0751 | 0.0695 | 0.9470 | | 0.3505 | 3050 | 0.0730 | - | - | | 0.3563 | 3100 | 0.0733 | - | - | | 0.3620 | 3150 | 0.0738 | - | - | | 0.3678 | 3200 | 0.0701 | - | - | | 0.3735 | 3250 | 0.0723 | - | - | | 0.3793 | 3300 | 0.0759 | - | - | | 0.3850 | 3350 | 0.0675 | - | - | | 0.3908 | 3400 | 0.0696 | - | - | | 0.3965 | 3450 | 0.0707 | - | - | | 0.4023 | 3500 | 0.0705 | 0.0669 | 0.9440 | | 0.4080 | 3550 | 0.0702 | - | - | | 0.4137 | 3600 | 0.0716 | - | - | | 0.4195 | 3650 | 0.0697 | - | - | | 0.4252 | 3700 | 0.0721 | - | - | | 0.4310 | 3750 | 0.0723 | - | - | | 0.4367 | 3800 | 0.0741 | - | - | | 0.4425 | 3850 | 0.0702 | - | - | | 0.4482 | 3900 | 0.0653 | - | - | | 0.4540 | 3950 | 0.0704 | - | - | | 0.4597 | 4000 | 0.0718 | 0.0652 | 0.9450 | | 0.4655 | 4050 | 0.0683 | - | - | | 0.4712 | 4100 | 0.0719 | - | - | | 0.4770 | 4150 | 0.0674 | - | - | | 0.4827 | 4200 | 0.0659 | - | - | | 0.4884 | 4250 | 0.0735 | - | - | | 0.4942 | 4300 | 0.0737 | - | - | | 0.4999 | 4350 | 0.0707 | - | - | | 0.5057 | 4400 | 0.0690 | - | - | | 0.5114 | 4450 | 0.0707 | - | - | | 0.5172 | 4500 | 0.0696 | 0.0637 | 0.9470 | | 0.5229 | 4550 | 0.0686 | - | - | | 0.5287 | 4600 | 0.0710 | - | - | | 0.5344 | 4650 | 0.0681 | - | - | | 0.5402 | 4700 | 0.0667 | - | - | | 0.5459 | 4750 | 0.0673 | - | - | | 0.5517 | 4800 | 0.0618 | - | - | | 0.5574 | 4850 | 0.0715 | - | - | | 0.5632 | 4900 | 0.0703 | - | - | | 0.5689 | 4950 | 0.0675 | - | - | | 0.5746 | 5000 | 0.0715 | 0.0638 | 0.9500 | | 0.5804 | 5050 | 0.0681 | - | - | | 0.5861 | 5100 | 0.0628 | - | - | | 0.5919 | 5150 | 0.0654 | - | - | | 0.5976 | 5200 | 0.0662 | - | - | | 0.6034 | 5250 | 0.0626 | - | - | | 0.6091 | 5300 | 0.0660 | - | - | | 0.6149 | 5350 | 0.0652 | - | - | | 0.6206 | 5400 | 0.0687 | - | - | | 0.6264 | 5450 | 0.0677 | - | - | | 0.6321 | 5500 | 0.0683 | 0.0631 | 0.9530 | | 0.6379 | 5550 | 0.0666 | - | - | | 0.6436 | 5600 | 0.0663 | - | - | | 0.6494 | 5650 | 0.0637 | - | - | | 0.6551 | 5700 | 0.0687 | - | - | | 0.6608 | 5750 | 0.0620 | - | - | | 0.6666 | 5800 | 0.0664 | - | - | | 0.6723 | 5850 | 0.0666 | - | - | | 0.6781 | 5900 | 0.0632 | - | - | | 0.6838 | 5950 | 0.0676 | - | - | | 0.6896 | 6000 | 0.0638 | 0.0634 | 0.9530 | | 0.6953 | 6050 | 0.0655 | - | - | | 0.7011 | 6100 | 0.0651 | - | - | | 0.7068 | 6150 | 0.0675 | - | - | | 0.7126 | 6200 | 0.0685 | - | - | | 0.7183 | 6250 | 0.0647 | - | - | | 0.7241 | 6300 | 0.0609 | - | - | | 0.7298 | 6350 | 0.0643 | - | - | | 0.7355 | 6400 | 0.0628 | - | - | | 0.7413 | 6450 | 0.0627 | - | - | | **0.747** | **6500** | **0.0639** | **0.0621** | **0.954** | | 0.7528 | 6550 | 0.0658 | - | - | | 0.7585 | 6600 | 0.0667 | - | - | | 0.7643 | 6650 | 0.0632 | - | - | | 0.7700 | 6700 | 0.0616 | - | - | | 0.7758 | 6750 | 0.0666 | - | - | | 0.7815 | 6800 | 0.0634 | - | - | | 0.7873 | 6850 | 0.0647 | - | - | | 0.7930 | 6900 | 0.0644 | - | - | | 0.7988 | 6950 | 0.0617 | - | - | | 0.8045 | 7000 | 0.0677 | 0.0626 | 0.9510 | | 0.8103 | 7050 | 0.0616 | - | - | | 0.8160 | 7100 | 0.0633 | - | - | | 0.8217 | 7150 | 0.0645 | - | - | | 0.8275 | 7200 | 0.0656 | - | - | | 0.8332 | 7250 | 0.0597 | - | - | | 0.8390 | 7300 | 0.0670 | - | - | | 0.8447 | 7350 | 0.0638 | - | - | | 0.8505 | 7400 | 0.0641 | - | - | | 0.8562 | 7450 | 0.0660 | - | - | | 0.8620 | 7500 | 0.0687 | 0.0618 | 0.9490 | | 0.8677 | 7550 | 0.0654 | - | - | | 0.8735 | 7600 | 0.0633 | - | - | | 0.8792 | 7650 | 0.0660 | - | - | | 0.8850 | 7700 | 0.0674 | - | - | | 0.8907 | 7750 | 0.0681 | - | - | | 0.8964 | 7800 | 0.0601 | - | - | | 0.9022 | 7850 | 0.0612 | - | - | | 0.9079 | 7900 | 0.0626 | - | - | | 0.9137 | 7950 | 0.0641 | - | - | | 0.9194 | 8000 | 0.0633 | 0.0619 | 0.9470 | | 0.9252 | 8050 | 0.0637 | - | - | | 0.9309 | 8100 | 0.0630 | - | - | | 0.9367 | 8150 | 0.0646 | - | - | | 0.9424 | 8200 | 0.0648 | - | - | | 0.9482 | 8250 | 0.0647 | - | - | | 0.9539 | 8300 | 0.0601 | - | - | | 0.9597 | 8350 | 0.0600 | - | - | | 0.9654 | 8400 | 0.0668 | - | - | | 0.9712 | 8450 | 0.0640 | - | - | | 0.9769 | 8500 | 0.0579 | 0.0618 | 0.9500 | | 0.9826 | 8550 | 0.0645 | - | - | | 0.9884 | 8600 | 0.0614 | - | - | | 0.9941 | 8650 | 0.0642 | - | - | | 0.9999 | 8700 | 0.0652 | - | - | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.12.12 - Sentence Transformers: 5.2.2 - Transformers: 5.0.0 - PyTorch: 2.9.0+cu128 - Accelerate: 1.12.0 - Datasets: 4.0.0 - Tokenizers: 0.22.2 ## 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", } ```