--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:99000 - loss:SpladeLoss - loss:SparseDistillKLDivLoss - loss:FlopsLoss base_model: Luyu/co-condenser-marco widget: - text: 'The ejection fraction may decrease if: 1 You have weakness of your heart muscle, such as dilated cardiomyopathy, which can be caused by a heart muscle problem, familial (genetic) cardiomyopathy, or systemic illnesses. 2 A heart attack has damaged your heart. You have problems with your heart''s valves.' - text: "One thing we avoided: Lots of alternative slime recipes swap Borax for liquid\ \ starch, shampoo, body wash, hand soap, contact lens solution, or laundry detergent.\ \ Those may seem benign â\x80\x94 and they might be â\x80\x94 but many of them\ \ contain derivatives or relatives of sodium borate too." - text: how do i get my mvr in pa - text: English is a language whose vocabulary is the composite of a surprising range of influences. We have pillaged words from Latin, Greek, Dutch, Arabic, Old Norse, Spanish, Italian, Hindi, and more besides to make English what it is today. - text: Weed Eater was a string trimmer company founded in 1971 in Houston, Texas by George C. Ballas, Sr. , the inventor of the device. The idea for the Weed Eater trimmer came to him from the spinning nylon bristles of an automatic car wash.He thought that he could come up with a similar technique to protect the bark on trees that he was trimming around. His company was eventually bought by Emerson Electric and merged with Poulan.Poulan/Weed Eater was later purchased by Electrolux, which spun off the outdoors division as Husqvarna AB in 2006.Inventor Ballas was the father of champion ballroom dancer Corky Ballas and the grandfather of Dancing with the Stars dancer Mark Ballas.George Ballas died on June 25, 2011.he idea for the Weed Eater trimmer came to him from the spinning nylon bristles of an automatic car wash. He thought that he could come up with a similar technique to protect the bark on trees that he was trimming around. His company was eventually bought by Emerson Electric and merged with Poulan. pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 73.87900982296586 energy_consumed: 0.19006593694646762 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.531 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: CoCondenser finetuned on MS MARCO results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.15600000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.78 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6078319139663582 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5333015873015872 name: Dot Mrr@10 - type: dot_map@100 value: 0.5429181880921011 name: Dot Map@100 - type: query_active_dims value: 24.360000610351562 name: Query Active Dims - type: query_sparsity_ratio value: 0.9992018871433604 name: Query Sparsity Ratio - type: corpus_active_dims value: 264.70611572265625 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9913273666298847 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.36666666666666664 name: Dot Precision@3 - type: dot_precision@5 value: 0.324 name: Dot Precision@5 - type: dot_precision@10 value: 0.27 name: Dot Precision@10 - type: dot_recall@1 value: 0.06223547543970161 name: Dot Recall@1 - type: dot_recall@3 value: 0.09468182448150209 name: Dot Recall@3 - type: dot_recall@5 value: 0.11080815285177574 name: Dot Recall@5 - type: dot_recall@10 value: 0.13852886366526052 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.349956209575989 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5455555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.16563763914899593 name: Dot Map@100 - type: query_active_dims value: 19.760000228881836 name: Query Active Dims - type: query_sparsity_ratio value: 0.999352598118443 name: Query Sparsity Ratio - type: corpus_active_dims value: 498.65423583984375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.983662465243436 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.2333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.16399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.45 name: Dot Recall@1 - type: dot_recall@3 value: 0.65 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6400973977443337 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5997460317460317 name: Dot Mrr@10 - type: dot_map@100 value: 0.5785145426544593 name: Dot Map@100 - type: query_active_dims value: 27.8799991607666 name: Query Active Dims - type: query_sparsity_ratio value: 0.9990865605412238 name: Query Sparsity Ratio - type: corpus_active_dims value: 300.7279052734375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9901471756348392 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.4466666666666666 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6533333333333333 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7266666666666666 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7866666666666666 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4466666666666666 name: Dot Precision@1 - type: dot_precision@3 value: 0.2733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.21466666666666667 name: Dot Precision@5 - type: dot_precision@10 value: 0.14866666666666664 name: Dot Precision@10 - type: dot_recall@1 value: 0.2974118251465672 name: Dot Recall@1 - type: dot_recall@3 value: 0.4682272748271674 name: Dot Recall@3 - type: dot_recall@5 value: 0.5436027176172585 name: Dot Recall@5 - type: dot_recall@10 value: 0.5995096212217534 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5326285070955602 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5595343915343914 name: Dot Mrr@10 - type: dot_map@100 value: 0.4290234566318521 name: Dot Map@100 - type: query_active_dims value: 24.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9992136819343425 name: Query Sparsity Ratio - type: corpus_active_dims value: 331.6402350607146 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9891343871613684 name: Corpus Sparsity Ratio --- # CoCondenser finetuned on MS MARCO This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) 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:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **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': 512, 'do_lower_case': False, 'architecture': '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("tomaarsen/splade-cocondenser-msmarco-kldiv-minilm-temp-3-3") # Run inference queries = [ "who started gladiator lacrosse", ] documents = [ 'Weed Eater was a string trimmer company founded in 1971 in Houston, Texas by George C. Ballas, Sr. , the inventor of the device. The idea for the Weed Eater trimmer came to him from the spinning nylon bristles of an automatic car wash.He thought that he could come up with a similar technique to protect the bark on trees that he was trimming around. His company was eventually bought by Emerson Electric and merged with Poulan.Poulan/Weed Eater was later purchased by Electrolux, which spun off the outdoors division as Husqvarna AB in 2006.Inventor Ballas was the father of champion ballroom dancer Corky Ballas and the grandfather of Dancing with the Stars dancer Mark Ballas.George Ballas died on June 25, 2011.he idea for the Weed Eater trimmer came to him from the spinning nylon bristles of an automatic car wash. He thought that he could come up with a similar technique to protect the bark on trees that he was trimming around. His company was eventually bought by Emerson Electric and merged with Poulan.', "The earliest types of gladiator were named after Rome's enemies of that time: the Samnite, Thracian and Gaul. The Samnite, heavily armed, elegantly helmed and probably the most popular type, was renamed Secutor and the Gaul renamed Murmillo, once these former enemies had been conquered then absorbed into Rome's Empire.", 'Summit Hill, PA. Sponsored Topics. Summit Hill is a borough in Carbon County, Pennsylvania, United States. The population was 2,974 at the 2000 census. Summit Hill is located at 40°49â\x80²39â\x80³N 75°51â\x80²57â\x80³W / 40.8275°N 75.86583°W / 40.8275; -75.86583 (40.827420, -75.865892).', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 30522] [3, 30522] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[16.1470, 27.7565, 12.2730]]) ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | |:----------------------|:------------|:-------------|:-----------| | dot_accuracy@1 | 0.38 | 0.48 | 0.48 | | dot_accuracy@3 | 0.66 | 0.62 | 0.68 | | dot_accuracy@5 | 0.78 | 0.62 | 0.78 | | dot_accuracy@10 | 0.84 | 0.66 | 0.86 | | dot_precision@1 | 0.38 | 0.48 | 0.48 | | dot_precision@3 | 0.22 | 0.3667 | 0.2333 | | dot_precision@5 | 0.156 | 0.324 | 0.164 | | dot_precision@10 | 0.084 | 0.27 | 0.092 | | dot_recall@1 | 0.38 | 0.0622 | 0.45 | | dot_recall@3 | 0.66 | 0.0947 | 0.65 | | dot_recall@5 | 0.78 | 0.1108 | 0.74 | | dot_recall@10 | 0.84 | 0.1385 | 0.82 | | **dot_ndcg@10** | **0.6078** | **0.35** | **0.6401** | | dot_mrr@10 | 0.5333 | 0.5456 | 0.5997 | | dot_map@100 | 0.5429 | 0.1656 | 0.5785 | | query_active_dims | 24.36 | 19.76 | 27.88 | | query_sparsity_ratio | 0.9992 | 0.9994 | 0.9991 | | corpus_active_dims | 264.7061 | 498.6542 | 300.7279 | | corpus_sparsity_ratio | 0.9913 | 0.9837 | 0.9901 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.4467 | | dot_accuracy@3 | 0.6533 | | dot_accuracy@5 | 0.7267 | | dot_accuracy@10 | 0.7867 | | dot_precision@1 | 0.4467 | | dot_precision@3 | 0.2733 | | dot_precision@5 | 0.2147 | | dot_precision@10 | 0.1487 | | dot_recall@1 | 0.2974 | | dot_recall@3 | 0.4682 | | dot_recall@5 | 0.5436 | | dot_recall@10 | 0.5995 | | **dot_ndcg@10** | **0.5326** | | dot_mrr@10 | 0.5595 | | dot_map@100 | 0.429 | | query_active_dims | 24.0 | | query_sparsity_ratio | 0.9992 | | corpus_active_dims | 331.6402 | | corpus_sparsity_ratio | 0.9891 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 99,000 training samples * Columns: query, positive, negative, and label * Approximate statistics based on the first 1000 samples: | | query | positive | negative | label | |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------| | type | string | string | string | list | | details | | | | | * Samples: | query | positive | negative | label | |:---------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------| | rtn tv network | Home Shopping Network. Home Shopping Network (HSN) is an American broadcast, basic cable and satellite television network that is owned by HSN, Inc. (NASDAQ: HSNI), which also owns catalog company Cornerstone Brands. Based in St. Petersburg, Florida, United States, the home shopping channel has former and current sister channels in several other countries. | The Public Switched Telephone Network - The public switched telephone network (PSTN) is the international network of circuit-switched telephones. Learn more about PSTN at HowStuffWorks. x | [-1.0804121494293213, -5.908488750457764] | | how did president nixon react to the watergate investigation? | The Watergate scandal was a major political scandal that occurred in the United States during the early 1970s, following a break-in by five men at the Democratic National Committee headquarters at the Watergate office complex in Washington, D.C. on June 17, 1972, and President Richard Nixon's administration's subsequent attempt to cover up its involvement. 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From my research, Pennsylvania law defines bodily injury as the impairment of physical condition or substantial pain. | [-8.954689025878906, -1.3361705541610718] | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseDistillKLDivLoss", "lambda_corpus": 0.0005, "lambda_query": 0.0005 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 1,000 evaluation samples * Columns: query, positive, negative, and label * Approximate statistics based on the first 1000 samples: | | query | positive | negative | label | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------| | type | string | string | string | list | | details | | | | | * Samples: | query | positive | negative | label | |:-----------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------| | how long to cook roast beef for | Roasting times for beef. Preheat your oven to 160°C (325°F) and use these cooking times to prepare a roast that's moist, tender and delicious. Your roast should be covered with foil for the first half of the roasting time to prevent drying the outer layer.3 to 5lb Joint 1½ to 2 hours.reheat your oven to 160°C (325°F) and use these cooking times to prepare a roast that's moist, tender and delicious. Your roast should be covered with foil for the first half of the roasting time to prevent drying the outer layer. | Estimating Cooking Time for Large Beef Roasts. If you roast at a steady 325F (160C), subtract 2 minutes or so per pound. If the roast is refrigerated just before going into the oven, add 2 or 3 minutes per pound. WARNING NOTES: Remember, the rib roast will continue to cook as it sets. | [6.501978874206543, 8.214995384216309] | | definition of fire inspection | Learn how to do a monthly fire extinguisher inspection in your workplace. Departments must assign an individual to inspect monthly the extinguishers in or adjacent to the department's facilities.1 Read Fire Extinguisher Types and Maintenance for more information.earn how to do a monthly fire extinguisher inspection in your workplace. Departments must assign an individual to inspect monthly the extinguishers in or adjacent to the department's facilities. | reconnaissance by fire-a method of reconnaissance in which fire is placed on a suspected enemy position in order to cause the enemy to disclose his presence by moving or returning fire. reconnaissance in force-an offensive operation designed to discover or test the enemy's strength (or to obtain other information). mission undertaken to obtain, by visual observation or other detection methods, information about the activities and resources of an enemy or potential enemy, or to secure data concerning the meteorological, hydrographic, or geographic characteristics of a particular area. | [-0.38299351930618286, -0.9372650384902954] | | how many stores does family dollar have | Property Spotlight: New Retail Center at Hamilton & Warner - Outlots Available!! Family Dollar is closing stores following a disappointing second quarter. 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Read employee reviews and ratings on Glassdoor to decide if Family Dollar Stores is right for you. | [4.726407527923584, 8.284608840942383] | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseDistillKLDivLoss", "lambda_corpus": 0.0005, "lambda_query": 0.0005 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `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`: 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`: 2e-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 - `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 - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:| | 0.0162 | 100 | 544.3124 | - | - | - | - | - | | 0.0323 | 200 | 64.047 | - | - | - | - | - | | 0.0485 | 300 | 3.1121 | - | - | - | - | - | | 0.0646 | 400 | 1.6162 | - | - | - | - | - | | 0.0808 | 500 | 1.5318 | 1.4599 | 0.0824 | 0.1198 | 0.1638 | 0.1220 | | 0.0970 | 600 | 1.4639 | - | - | - | - | - | | 0.1131 | 700 | 1.3349 | - | - | - | - | - | | 0.1293 | 800 | 1.2342 | - | - | - | - | - | | 0.1454 | 900 | 1.2704 | - | - | - | - | - | | 0.1616 | 1000 | 1.1347 | 1.1330 | 0.5389 | 0.3024 | 0.5391 | 0.4601 | | 0.1778 | 1100 | 1.1115 | - | - | - | - | - | | 0.1939 | 1200 | 1.142 | - | - | - | - | - | | 0.2101 | 1300 | 1.0762 | - | - | - | - | - | | 0.2262 | 1400 | 1.0613 | - | - | - | - | - | | 0.2424 | 1500 | 0.9862 | 0.8948 | 0.5493 | 0.2987 | 0.6166 | 0.4882 | | 0.2586 | 1600 | 1.0027 | - | - | - | - | - | | 0.2747 | 1700 | 0.9407 | - | - | - | - | - | | 0.2909 | 1800 | 0.9287 | - | - | - | - | - | | 0.3070 | 1900 | 0.9675 | - | - | - | - | - | | 0.3232 | 2000 | 0.8843 | 0.7885 | 0.5991 | 0.3346 | 0.5962 | 0.5099 | | 0.3394 | 2100 | 0.8368 | - | - | - | - | - | | 0.3555 | 2200 | 0.87 | - | - | - | - | - | | 0.3717 | 2300 | 0.8395 | - | - | - | - | - | | 0.3878 | 2400 | 0.8445 | - | - | - | - | - | | 0.4040 | 2500 | 0.8649 | 0.7284 | 0.5993 | 0.3537 | 0.6355 | 0.5295 | | 0.4202 | 2600 | 0.8638 | - | - | - | - | - | | 0.4363 | 2700 | 0.8144 | - | - | - | - | - | | 0.4525 | 2800 | 0.8155 | - | - | - | - | - | | 0.4686 | 2900 | 0.7406 | - | - | - | - | - | | 0.4848 | 3000 | 0.7348 | 0.7507 | 0.5996 | 0.3274 | 0.6156 | 0.5142 | | 0.5010 | 3100 | 0.7925 | - | - | - | - | - | | 0.5171 | 3200 | 0.7248 | - | - | - | - | - | | 0.5333 | 3300 | 0.7875 | - | - | - | - | - | | 0.5495 | 3400 | 0.7392 | - | - | - | - | - | | 0.5656 | 3500 | 0.7692 | 0.6347 | 0.6148 | 0.3276 | 0.6370 | 0.5265 | | 0.5818 | 3600 | 0.7236 | - | - | - | - | - | | 0.5979 | 3700 | 0.7718 | - | - | - | - | - | | 0.6141 | 3800 | 0.7091 | - | - | - | - | - | | 0.6303 | 3900 | 0.7418 | - | - | - | - | - | | 0.6464 | 4000 | 0.673 | 0.6148 | 0.6184 | 0.3498 | 0.6114 | 0.5265 | | 0.6626 | 4100 | 0.7132 | - | - | - | - | - | | 0.6787 | 4200 | 0.6932 | - | - | - | - | - | | 0.6949 | 4300 | 0.7174 | - | - | - | - | - | | 0.7111 | 4400 | 0.6824 | - | - | - | - | - | | 0.7272 | 4500 | 0.7167 | 0.5994 | 0.6391 | 0.3254 | 0.6251 | 0.5299 | | 0.7434 | 4600 | 0.7299 | - | - | - | - | - | | 0.7595 | 4700 | 0.6442 | - | - | - | - | - | | 0.7757 | 4800 | 0.688 | - | - | - | - | - | | 0.7919 | 4900 | 0.6864 | - | - | - | - | - | | 0.8080 | 5000 | 0.6575 | 0.5827 | 0.6315 | 0.3352 | 0.6469 | 0.5379 | | 0.8242 | 5100 | 0.656 | - | - | - | - | - | | 0.8403 | 5200 | 0.604 | - | - | - | - | - | | 0.8565 | 5300 | 0.7024 | - | - | - | - | - | | 0.8727 | 5400 | 0.6375 | - | - | - | - | - | | 0.8888 | 5500 | 0.6337 | 0.5852 | 0.6284 | 0.3509 | 0.6349 | 0.5381 | | 0.9050 | 5600 | 0.6636 | - | - | - | - | - | | 0.9211 | 5700 | 0.6451 | - | - | - | - | - | | 0.9373 | 5800 | 0.6446 | - | - | - | - | - | | 0.9535 | 5900 | 0.6194 | - | - | - | - | - | | 0.9696 | 6000 | 0.657 | 0.5775 | 0.6072 | 0.3514 | 0.6421 | 0.5336 | | 0.9858 | 6100 | 0.6887 | - | - | - | - | - | | -1 | -1 | - | - | 0.6078 | 0.3500 | 0.6401 | 0.5326 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.190 kWh - **Carbon Emitted**: 0.074 kg of CO2 - **Hours Used**: 0.531 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.7.1+cu126 - Accelerate: 1.5.1 - Datasets: 2.21.0 - 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}, } ``` #### SparseDistillKLDivLoss ```bibtex @misc{lin2020distillingdenserepresentationsranking, title={Distilling Dense Representations for Ranking using Tightly-Coupled Teachers}, author={Sheng-Chieh Lin and Jheng-Hong Yang and Jimmy Lin}, year={2020}, eprint={2010.11386}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2010.11386}, } ``` #### 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} } ```