--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l-v2.0 widget: - source_sentence: A construction worker is standing on a crane placing a large arm on top of a stature in progress. sentences: - A man is playing with his camera. - A person standing - Nobody is standing - source_sentence: A boy in red slides down an inflatable ride. sentences: - a baby smiling - A boy is playing on an inflatable ride. - A boy pierces a knife through an inflatable ride. - source_sentence: A man in a black shirt is playing a guitar. sentences: - A group of women are selling their wares - The man is wearing black. - The man is wearing a blue shirt. - source_sentence: A man with a large power drill standing next to his daughter with a vacuum cleaner hose. sentences: - A man holding a drill stands next to a girl holding a vacuum hose. - Kids ride an amusement ride. - The man and girl are painting the walls. - source_sentence: A middle-aged man works under the engine of a train on rail tracks. sentences: - A guy is working on a train. - Two young asian men are squatting. - A guy is driving to work. datasets: - sentence-transformers/all-nli pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0 results: - task: type: triplet name: Triplet dataset: name: all nli test type: all-nli-test metrics: - type: cosine_accuracy value: 0.9558178241791496 name: Cosine Accuracy --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 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/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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("JatinkInnovision/snowflake-arctic-embed-l-v2.0_all-nli") # Run inference sentences = [ 'A middle-aged man works under the engine of a train on rail tracks.', 'A guy is working on a train.', 'A guy is driving to work.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Triplet * Dataset: `all-nli-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9558** | ## 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: 557,850 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. | | Children smiling and waving at camera | There are children present | The kids are frowning | | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | The boy skates down the sidewalk. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### 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: 6,584 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding packages. | The men are fighting outside a deli. | | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | Two kids in jackets walk to school. | | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | A woman drinks her coffee in a small cafe. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 50 - `per_device_eval_batch_size`: 50 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: 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`: 50 - `per_device_eval_batch_size`: 50 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `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`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `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`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | all-nli-test_cosine_accuracy | |:------:|:-----:|:-------------:|:---------------:|:----------------------------:| | 0.0090 | 100 | 1.8838 | 0.6502 | - | | 0.0179 | 200 | 1.2991 | 0.6177 | - | | 0.0269 | 300 | 1.2721 | 0.6417 | - | | 0.0359 | 400 | 1.2265 | 0.7053 | - | | 0.0448 | 500 | 1.0111 | 0.7147 | - | | 0.0538 | 600 | 1.0491 | 0.7457 | - | | 0.0627 | 700 | 1.0186 | 0.7922 | - | | 0.0717 | 800 | 1.135 | 0.8940 | - | | 0.0807 | 900 | 1.0747 | 0.7007 | - | | 0.0896 | 1000 | 0.9373 | 0.7298 | - | | 0.0986 | 1100 | 0.9572 | 0.6809 | - | | 0.1076 | 1200 | 1.1316 | 0.7260 | - | | 0.1165 | 1300 | 0.9188 | 0.7085 | - | | 0.1255 | 1400 | 0.9554 | 0.6876 | - | | 0.1344 | 1500 | 0.9494 | 0.7492 | - | | 0.1434 | 1600 | 0.811 | 0.7234 | - | | 0.1524 | 1700 | 0.7766 | 0.6744 | - | | 0.1613 | 1800 | 0.9317 | 0.7178 | - | | 0.1703 | 1900 | 0.9148 | 0.6960 | - | | 0.1793 | 2000 | 0.8643 | 0.6642 | - | | 0.1882 | 2100 | 0.7604 | 0.6425 | - | | 0.1972 | 2200 | 0.776 | 0.6347 | - | | 0.2061 | 2300 | 0.8286 | 0.6581 | - | | 0.2151 | 2400 | 0.8946 | 0.5866 | - | | 0.2241 | 2500 | 0.8507 | 0.6845 | - | | 0.2330 | 2600 | 0.7917 | 0.6091 | - | | 0.2420 | 2700 | 0.8192 | 0.7073 | - | | 0.2510 | 2800 | 0.8818 | 0.6584 | - | | 0.2599 | 2900 | 0.8261 | 0.6112 | - | | 0.2689 | 3000 | 0.8017 | 0.6883 | - | | 0.2779 | 3100 | 0.8147 | 0.6450 | - | | 0.2868 | 3200 | 0.8297 | 0.6086 | - | | 0.2958 | 3300 | 0.7516 | 0.5857 | - | | 0.3047 | 3400 | 0.8628 | 0.6061 | - | | 0.3137 | 3500 | 0.7758 | 0.5751 | - | | 0.3227 | 3600 | 0.7773 | 0.6022 | - | | 0.3316 | 3700 | 0.7559 | 0.5446 | - | | 0.3406 | 3800 | 0.796 | 0.5842 | - | | 0.3496 | 3900 | 0.8295 | 0.5822 | - | | 0.3585 | 4000 | 0.7292 | 0.5821 | - | | 0.3675 | 4100 | 0.7475 | 0.6358 | - | | 0.3764 | 4200 | 0.7916 | 0.5688 | - | | 0.3854 | 4300 | 0.7214 | 0.5653 | - | | 0.3944 | 4400 | 0.704 | 0.5564 | - | | 0.4033 | 4500 | 0.7817 | 0.5876 | - | | 0.4123 | 4600 | 0.7549 | 0.5358 | - | | 0.4213 | 4700 | 0.7206 | 0.5785 | - | | 0.4302 | 4800 | 0.7462 | 0.5568 | - | | 0.4392 | 4900 | 0.665 | 0.5765 | - | | 0.4481 | 5000 | 0.7743 | 0.5303 | - | | 0.4571 | 5100 | 0.7055 | 0.5733 | - | | 0.4661 | 5200 | 0.7004 | 0.6280 | - | | 0.4750 | 5300 | 0.7021 | 0.5444 | - | | 0.4840 | 5400 | 0.6858 | 0.5787 | - | | 0.4930 | 5500 | 0.7007 | 0.6124 | - | | 0.5019 | 5600 | 0.6722 | 0.5705 | - | | 0.5109 | 5700 | 0.7124 | 0.5440 | - | | 0.5199 | 5800 | 0.6657 | 0.5262 | - | | 0.5288 | 5900 | 0.6784 | 0.5400 | - | | 0.5378 | 6000 | 0.6644 | 0.5093 | - | | 0.5467 | 6100 | 0.7195 | 0.5453 | - | | 0.5557 | 6200 | 0.6958 | 0.5216 | - | | 0.5647 | 6300 | 0.7202 | 0.5250 | - | | 0.5736 | 6400 | 0.6921 | 0.5089 | - | | 0.5826 | 6500 | 0.6926 | 0.5207 | - | | 0.5916 | 6600 | 0.714 | 0.5084 | - | | 0.6005 | 6700 | 0.6605 | 0.4943 | - | | 0.6095 | 6800 | 0.7222 | 0.5058 | - | | 0.6184 | 6900 | 0.7171 | 0.4950 | - | | 0.6274 | 7000 | 0.6344 | 0.5110 | - | | 0.6364 | 7100 | 0.7057 | 0.5197 | - | | 0.6453 | 7200 | 0.6895 | 0.5096 | - | | 0.6543 | 7300 | 0.7226 | 0.4819 | - | | 0.6633 | 7400 | 0.6725 | 0.4780 | - | | 0.6722 | 7500 | 0.7469 | 0.5145 | - | | 0.6812 | 7600 | 0.7016 | 0.4969 | - | | 0.6901 | 7700 | 0.6655 | 0.4965 | - | | 0.6991 | 7800 | 0.7281 | 0.4913 | - | | 0.7081 | 7900 | 0.6748 | 0.5121 | - | | 0.7170 | 8000 | 0.6505 | 0.5207 | - | | 0.7260 | 8100 | 0.6594 | 0.4823 | - | | 0.7350 | 8200 | 0.7042 | 0.4903 | - | | 0.7439 | 8300 | 0.6995 | 0.4630 | - | | 0.7529 | 8400 | 0.634 | 0.4217 | - | | 0.7619 | 8500 | 0.3772 | 0.3684 | - | | 0.7708 | 8600 | 0.3416 | 0.3585 | - | | 0.7798 | 8700 | 0.3113 | 0.3471 | - | | 0.7887 | 8800 | 0.2793 | 0.3379 | - | | 0.7977 | 8900 | 0.2577 | 0.3349 | - | | 0.8067 | 9000 | 0.249 | 0.3320 | - | | 0.8156 | 9100 | 0.2191 | 0.3290 | - | | 0.8246 | 9200 | 0.2492 | 0.3255 | - | | 0.8336 | 9300 | 0.2464 | 0.3258 | - | | 0.8425 | 9400 | 0.2288 | 0.3247 | - | | 0.8515 | 9500 | 0.2132 | 0.3248 | - | | 0.8604 | 9600 | 0.2173 | 0.3259 | - | | 0.8694 | 9700 | 0.2008 | 0.3223 | - | | 0.8784 | 9800 | 0.2016 | 0.3219 | - | | 0.8873 | 9900 | 0.1962 | 0.3195 | - | | 0.8963 | 10000 | 0.1952 | 0.3185 | - | | 0.9053 | 10100 | 0.1959 | 0.3158 | - | | 0.9142 | 10200 | 0.2002 | 0.3138 | - | | 0.9232 | 10300 | 0.1882 | 0.3150 | - | | 0.9322 | 10400 | 0.1856 | 0.3124 | - | | 0.9411 | 10500 | 0.1971 | 0.3143 | - | | 0.9501 | 10600 | 0.1918 | 0.3137 | - | | 0.9590 | 10700 | 0.1825 | 0.3147 | - | | 0.9680 | 10800 | 0.1762 | 0.3155 | - | | 0.9770 | 10900 | 0.1778 | 0.3139 | - | | 0.9859 | 11000 | 0.1659 | 0.3138 | - | | 0.9949 | 11100 | 0.1848 | 0.3131 | - | | 1.0 | 11157 | - | - | 0.9558 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## 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", } ``` #### 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} } ```