--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - asymmetric - inference-free - splade - generated_from_trainer - dataset_size:99000 - loss:SpladeLoss - loss:SparseMultipleNegativesRankingLoss - loss:FlopsLoss base_model: distilbert/distilbert-base-uncased widget: - text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - text: Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions 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 model-index: - name: Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions tuples results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.1733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.12000000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.52 name: Dot Recall@3 - type: dot_recall@5 value: 0.6 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5294275268594165 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.44701587301587303 name: Dot Mrr@10 - type: dot_map@100 value: 0.4547435439455525 name: Dot Map@100 - type: query_active_dims value: 6.380000114440918 name: Query Active Dims - type: query_sparsity_ratio value: 0.9997909704437966 name: Query Sparsity Ratio - type: corpus_active_dims value: 56.05611801147461 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9981634192382061 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.48 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.54 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32800000000000007 name: Dot Precision@5 - type: dot_precision@10 value: 0.24600000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.04296449405849682 name: Dot Recall@1 - type: dot_recall@3 value: 0.07246863989183633 name: Dot Recall@3 - type: dot_recall@5 value: 0.09285358111876901 name: Dot Recall@5 - type: dot_recall@10 value: 0.11634922767333658 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.31292844524261265 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.47585714285714287 name: Dot Mrr@10 - type: dot_map@100 value: 0.13754623990324893 name: Dot Map@100 - type: query_active_dims value: 4.760000228881836 name: Query Active Dims - type: query_sparsity_ratio value: 0.999844046909479 name: Query Sparsity Ratio - type: corpus_active_dims value: 69.88655853271484 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9977102890199622 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666664 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07600000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.37 name: Dot Recall@1 - type: dot_recall@3 value: 0.58 name: Dot Recall@3 - type: dot_recall@5 value: 0.64 name: Dot Recall@5 - type: dot_recall@10 value: 0.71 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5476944409397304 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5072222222222222 name: Dot Mrr@10 - type: dot_map@100 value: 0.4973273986984246 name: Dot Map@100 - type: query_active_dims value: 9.4399995803833 name: Query Active Dims - type: query_sparsity_ratio value: 0.9996907149079227 name: Query Sparsity Ratio - type: corpus_active_dims value: 51.11539077758789 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.998325293533268 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: 0.38000000000000006 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6066666666666668 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7066666666666667 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38000000000000006 name: Dot Precision@1 - type: dot_precision@3 value: 0.24444444444444444 name: Dot Precision@3 - type: dot_precision@5 value: 0.19466666666666668 name: Dot Precision@5 - type: dot_precision@10 value: 0.134 name: Dot Precision@10 - type: dot_recall@1 value: 0.24432149801949896 name: Dot Recall@1 - type: dot_recall@3 value: 0.39082287996394544 name: Dot Recall@3 - type: dot_recall@5 value: 0.4442845270395897 name: Dot Recall@5 - type: dot_recall@10 value: 0.5421164092244455 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.46335013768058647 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4766984126984127 name: Dot Mrr@10 - type: dot_map@100 value: 0.36320572751574204 name: Dot Map@100 - type: query_active_dims value: 6.859999974568685 name: Query Active Dims - type: query_sparsity_ratio value: 0.9997752440870661 name: Query Sparsity Ratio - type: corpus_active_dims value: 57.281252631734205 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9981232798430071 name: Corpus Sparsity Ratio --- # Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions tuples This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) 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:** Asymmetric Inference-free SPLADE Sparse Encoder - **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **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): Router( (query_0_SparseStaticEmbedding): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=DistilBertTokenizerFast) (document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'}) (document_1_SpladePooling): 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("seank333111/inference-free-splade-distilbert-base-uncased-nq") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", 'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.', ] 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([[5.9751, 0.2390, 0.0000]]) ``` ## 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 | 0.32 | 0.44 | 0.38 | | dot_accuracy@3 | 0.52 | 0.48 | 0.62 | | dot_accuracy@5 | 0.6 | 0.54 | 0.68 | | dot_accuracy@10 | 0.8 | 0.58 | 0.74 | | dot_precision@1 | 0.32 | 0.44 | 0.38 | | dot_precision@3 | 0.1733 | 0.3533 | 0.2067 | | dot_precision@5 | 0.12 | 0.328 | 0.136 | | dot_precision@10 | 0.08 | 0.246 | 0.076 | | dot_recall@1 | 0.32 | 0.043 | 0.37 | | dot_recall@3 | 0.52 | 0.0725 | 0.58 | | dot_recall@5 | 0.6 | 0.0929 | 0.64 | | dot_recall@10 | 0.8 | 0.1163 | 0.71 | | **dot_ndcg@10** | **0.5294** | **0.3129** | **0.5477** | | dot_mrr@10 | 0.447 | 0.4759 | 0.5072 | | dot_map@100 | 0.4547 | 0.1375 | 0.4973 | | query_active_dims | 6.38 | 4.76 | 9.44 | | query_sparsity_ratio | 0.9998 | 0.9998 | 0.9997 | | corpus_active_dims | 56.0561 | 69.8866 | 51.1154 | | corpus_sparsity_ratio | 0.9982 | 0.9977 | 0.9983 | #### 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 | 0.38 | | dot_accuracy@3 | 0.54 | | dot_accuracy@5 | 0.6067 | | dot_accuracy@10 | 0.7067 | | dot_precision@1 | 0.38 | | dot_precision@3 | 0.2444 | | dot_precision@5 | 0.1947 | | dot_precision@10 | 0.134 | | dot_recall@1 | 0.2443 | | dot_recall@3 | 0.3908 | | dot_recall@5 | 0.4443 | | dot_recall@10 | 0.5421 | | **dot_ndcg@10** | **0.4634** | | dot_mrr@10 | 0.4767 | | dot_map@100 | 0.3632 | | query_active_dims | 6.86 | | query_sparsity_ratio | 0.9998 | | corpus_active_dims | 57.2813 | | corpus_sparsity_ratio | 0.9981 | ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who played the father in papa don't preach | Alex McArthur Alex McArthur (born March 6, 1957) is an American actor. | | where was the location of the battle of hastings | Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory. | | how many puppies can a dog give birth to | Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22] | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')", "document_regularizer_weight": 0.003, "query_regularizer_weight": 0 } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | where is the tiber river located in italy | Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks. | | what kind of car does jay gatsby drive | Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry. | | who sings if i can dream about you | I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1] | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')", "document_regularizer_weight": 0.003, "query_regularizer_weight": 0 } ``` ### 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 - `router_mapping`: {'query': 'query', 'answer': 'document'} - `learning_rate_mapping`: {'SparseStaticEmbedding\\.weight': 0.001} #### 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 - `hub_revision`: None - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {'query': 'query', 'answer': 'document'} - `learning_rate_mapping`: {'SparseStaticEmbedding\\.weight': 0.001}
### 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 | |:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:| | 0.0323 | 200 | 0.2418 | - | - | - | - | - | | 0.0646 | 400 | 0.0857 | - | - | - | - | - | | 0.0970 | 600 | 0.072 | - | - | - | - | - | | 0.1293 | 800 | 0.062 | - | - | - | - | - | | 0.1616 | 1000 | 0.0624 | 0.0867 | 0.5213 | 0.3326 | 0.5296 | 0.4612 | | 0.1939 | 1200 | 0.0684 | - | - | - | - | - | | 0.2262 | 1400 | 0.0776 | - | - | - | - | - | | 0.2586 | 1600 | 0.0824 | - | - | - | - | - | | 0.2909 | 1800 | 0.0826 | - | - | - | - | - | | 0.3232 | 2000 | 0.082 | 0.1028 | 0.5108 | 0.3230 | 0.5169 | 0.4502 | | 0.3555 | 2200 | 0.0869 | - | - | - | - | - | | 0.3878 | 2400 | 0.0866 | - | - | - | - | - | | 0.4202 | 2600 | 0.0848 | - | - | - | - | - | | 0.4525 | 2800 | 0.0816 | - | - | - | - | - | | 0.4848 | 3000 | 0.0769 | 0.0914 | 0.5667 | 0.3149 | 0.5786 | 0.4867 | | 0.5171 | 3200 | 0.0745 | - | - | - | - | - | | 0.5495 | 3400 | 0.0831 | - | - | - | - | - | | 0.5818 | 3600 | 0.0764 | - | - | - | - | - | | 0.6141 | 3800 | 0.0806 | - | - | - | - | - | | 0.6464 | 4000 | 0.0742 | 0.0885 | 0.5512 | 0.3221 | 0.5262 | 0.4665 | | 0.6787 | 4200 | 0.0739 | - | - | - | - | - | | 0.7111 | 4400 | 0.0674 | - | - | - | - | - | | 0.7434 | 4600 | 0.0675 | - | - | - | - | - | | 0.7757 | 4800 | 0.0741 | - | - | - | - | - | | 0.8080 | 5000 | 0.0686 | 0.0827 | 0.5514 | 0.3146 | 0.5632 | 0.4764 | | 0.8403 | 5200 | 0.0745 | - | - | - | - | - | | 0.8727 | 5400 | 0.0687 | - | - | - | - | - | | 0.9050 | 5600 | 0.0637 | - | - | - | - | - | | 0.9373 | 5800 | 0.0637 | - | - | - | - | - | | 0.9696 | 6000 | 0.0648 | 0.0785 | 0.5292 | 0.3117 | 0.5480 | 0.4630 | | -1 | -1 | - | - | 0.5294 | 0.3129 | 0.5477 | 0.4634 | ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.0.0 - Transformers: 4.53.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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", } ``` #### 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} } ```