--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:10000 - loss:SpladeLoss - loss:SparseMultipleNegativesRankingLoss - loss:FlopsLoss base_model: naver/splade-cocondenser-ensembledistil widget: - text: Two kids at a ballgame wash their hands. - text: Two dogs near a lake, while a person rides by on a horse. - text: This mother and her daughter and granddaughter are having car trouble, and the poor little girl looks hot out in the heat. - text: A young man competes in the Olympics in the pole vaulting competition. - text: A man is playing with the brass pots datasets: - sentence-transformers/all-nli pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - active_dims - sparsity_ratio co2_eq_emissions: emissions: 0.16583474956305416 energy_consumed: 0.0029592738907377744 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: AMD Ryzen 9 6900HX with Radeon Graphics ram_total_size: 30.6114501953125 hours_used: 0.025 hardware_used: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU model-index: - name: splade-cocondenser-ensembledistil trained on Natural Language Inference (NLI) results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8553775938865431 name: Pearson Cosine - type: spearman_cosine value: 0.8486465022828363 name: Spearman Cosine - type: active_dims value: 99.12466812133789 name: Active Dims - type: sparsity_ratio value: 0.9967523534459951 name: Sparsity Ratio - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8223180736705796 name: Pearson Cosine - type: spearman_cosine value: 0.8068358333807579 name: Spearman Cosine - type: active_dims value: 95.42276763916016 name: Active Dims - type: sparsity_ratio value: 0.9968736397470952 name: Sparsity Ratio --- # splade-cocondenser-ensembledistil trained on Natural Language Inference (NLI) This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [naver/splade-cocondenser-ensembledistil](https://huggingface.co/naver/splade-cocondenser-ensembledistil) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** SPLADE Sparse Encoder - **Base model:** [naver/splade-cocondenser-ensembledistil](https://huggingface.co/naver/splade-cocondenser-ensembledistil) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## 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 SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("arthurbresnu/example-splade-cocondenser-ensembledistil-nli") # Run inference sentences = [ 'A man is sitting in on the side of the street with brass pots.', 'A man is playing with the brass pots', 'A group of adults are swimming at the beach.', ] embeddings = model.encode(sentences) print(embeddings.shape) # (3, 30522) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `sts-dev` and `sts-test` * Evaluated with [SparseEmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator) | Metric | sts-dev | sts-test | |:--------------------|:-----------|:-----------| | pearson_cosine | 0.8554 | 0.8223 | | **spearman_cosine** | **0.8486** | **0.8068** | | active_dims | 99.1247 | 95.4228 | | sparsity_ratio | 0.9968 | 0.9969 | ## 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: 10,000 training 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 person on a horse jumps over a broken down airplane. | A person is training his horse for a competition. | 0.5 | | A person on a horse jumps over a broken down airplane. | A person is at a diner, ordering an omelette. | 0.0 | | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | 1.0 | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1, similarity_fct='dot_score')", "lambda_corpus": 0.003 } ``` ### 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: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:-----------------| | Two women are embracing while holding to go packages. | The sisters are hugging goodbye while holding to go packages after just eating lunch. | 0.5 | | Two women are embracing while holding to go packages. | Two woman are holding packages. | 1.0 | | Two women are embracing while holding to go packages. | The men are fighting outside a deli. | 0.0 | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1, similarity_fct='dot_score')", "lambda_corpus": 0.003 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 4e-06 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: 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`: 16 - `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 - `torch_empty_cache_steps`: None - `learning_rate`: 4e-06 - `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`: 0.0 - `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`: True - `fp16`: False - `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`: True - `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} - `tp_size`: 0 - `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 | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:--------:|:-------:|:-------------:|:---------------:|:-----------------------:|:------------------------:| | -1 | -1 | - | - | 0.8366 | - | | 0.032 | 20 | 1.0832 | - | - | - | | 0.064 | 40 | 0.8212 | - | - | - | | 0.096 | 60 | 0.796 | - | - | - | | 0.128 | 80 | 0.7953 | - | - | - | | 0.16 | 100 | 0.7574 | - | - | - | | 0.192 | 120 | 0.6197 | 0.6750 | 0.8443 | - | | 0.224 | 140 | 0.7125 | - | - | - | | 0.256 | 160 | 0.817 | - | - | - | | 0.288 | 180 | 0.7309 | - | - | - | | 0.32 | 200 | 0.639 | - | - | - | | 0.352 | 220 | 0.6873 | - | - | - | | 0.384 | 240 | 0.6973 | 0.6253 | 0.8471 | - | | 0.416 | 260 | 0.7197 | - | - | - | | 0.448 | 280 | 0.5894 | - | - | - | | 0.48 | 300 | 0.6682 | - | - | - | | 0.512 | 320 | 0.6064 | - | - | - | | 0.544 | 340 | 0.648 | - | - | - | | 0.576 | 360 | 0.6344 | 0.6071 | 0.8483 | - | | 0.608 | 380 | 0.5742 | - | - | - | | 0.64 | 400 | 0.4962 | - | - | - | | 0.672 | 420 | 0.4863 | - | - | - | | 0.704 | 440 | 0.5547 | - | - | - | | 0.736 | 460 | 0.6097 | - | - | - | | 0.768 | 480 | 0.6307 | 0.6027 | 0.8471 | - | | 0.8 | 500 | 0.6226 | - | - | - | | 0.832 | 520 | 0.6607 | - | - | - | | 0.864 | 540 | 0.526 | - | - | - | | 0.896 | 560 | 0.6036 | - | - | - | | 0.928 | 580 | 0.5897 | - | - | - | | **0.96** | **600** | **0.6395** | **0.5892** | **0.8486** | **-** | | 0.992 | 620 | 0.6069 | - | - | - | | -1 | -1 | - | - | - | 0.8068 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.003 kWh - **Carbon Emitted**: 0.000 kg of CO2 - **Hours Used**: 0.025 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU - **CPU Model**: AMD Ryzen 9 6900HX with Radeon Graphics - **RAM Size**: 30.61 GB ### Framework Versions - Python: 3.12.9 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.50.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## 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", } ``` #### SpladeLoss ```bibtex @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2205.04733}, } ``` #### SparseMultipleNegativesRankingLoss ```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} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ```