--- language: - en tags: - ColBERT - PyLate - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:443147 - loss:Distillation base_model: artiwise-ai/modernbert-base-tr-uncased datasets: - Speedsy/msmarco-cleaned-gemini-bge-tr-uncased pipeline_tag: sentence-similarity library_name: PyLate metrics: - MaxSim_accuracy@1 - MaxSim_accuracy@3 - MaxSim_accuracy@5 - MaxSim_accuracy@10 - MaxSim_precision@1 - MaxSim_precision@3 - MaxSim_precision@5 - MaxSim_precision@10 - MaxSim_recall@1 - MaxSim_recall@3 - MaxSim_recall@5 - MaxSim_recall@10 - MaxSim_ndcg@10 - MaxSim_mrr@10 - MaxSim_map@100 model-index: - name: PyLate model based on artiwise-ai/modernbert-base-tr-uncased results: - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: MaxSim_accuracy@1 value: 0.78 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.92 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.96 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 1.0 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.78 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.68 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.596 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.5459999999999999 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.08078717061354299 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.1904489241619047 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.26256917349788084 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.39256681253841286 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.6694382434315426 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.8612222222222222 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.5270972799616637 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: MaxSim_accuracy@1 value: 0.48 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.62 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.72 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.72 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.48 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.27999999999999997 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.21999999999999997 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.12999999999999998 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.25257936507936507 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.3990714285714285 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.510595238095238 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.5472063492063493 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.47985220902930087 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.5619999999999999 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.41362574871825997 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: MaxSim_accuracy@1 value: 0.92 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.98 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 1.0 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 1.0 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.92 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.5133333333333333 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.33599999999999997 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.17 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.46 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.77 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.84 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.85 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.8340361138357484 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.9516666666666667 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.7774992099056552 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: MaxSim_accuracy@1 value: 0.42 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.6 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.7 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.8 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.42 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.2 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.14 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.08 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.42 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.6 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.7 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.8 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.6031078965623429 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.5408333333333333 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.5486820427095569 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: MaxSim_accuracy@1 value: 0.58 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.7 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.76 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.84 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.58 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.24 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.15600000000000003 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.09 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.57 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.69 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.73 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.81 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.6918755447681874 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.6583571428571429 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.6540863099196654 name: Maxsim Map@100 - task: type: py-late-information-retrieval name: Py Late Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: MaxSim_accuracy@1 value: 0.42 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.62 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.66 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.78 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.42 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.29333333333333333 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.23199999999999998 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.158 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.08866666666666667 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.18166666666666664 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.2396666666666667 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.3246666666666666 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.3235935014165522 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.5337777777777777 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.24429363290034992 name: Maxsim Map@100 - task: type: pylate-custom-nano-beir name: Pylate Custom Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: MaxSim_accuracy@1 value: 0.6 name: Maxsim Accuracy@1 - type: MaxSim_accuracy@3 value: 0.7400000000000001 name: Maxsim Accuracy@3 - type: MaxSim_accuracy@5 value: 0.7999999999999999 name: Maxsim Accuracy@5 - type: MaxSim_accuracy@10 value: 0.8566666666666666 name: Maxsim Accuracy@10 - type: MaxSim_precision@1 value: 0.6 name: Maxsim Precision@1 - type: MaxSim_precision@3 value: 0.36777777777777776 name: Maxsim Precision@3 - type: MaxSim_precision@5 value: 0.27999999999999997 name: Maxsim Precision@5 - type: MaxSim_precision@10 value: 0.19566666666666666 name: Maxsim Precision@10 - type: MaxSim_recall@1 value: 0.31200553372659573 name: Maxsim Recall@1 - type: MaxSim_recall@3 value: 0.4718645032333333 name: Maxsim Recall@3 - type: MaxSim_recall@5 value: 0.5471385130432976 name: Maxsim Recall@5 - type: MaxSim_recall@10 value: 0.6207399714019047 name: Maxsim Recall@10 - type: MaxSim_ndcg@10 value: 0.6003172515072791 name: Maxsim Ndcg@10 - type: MaxSim_mrr@10 value: 0.6846428571428572 name: Maxsim Mrr@10 - type: MaxSim_map@100 value: 0.5275473706858586 name: Maxsim Map@100 --- # PyLate model based on artiwise-ai/modernbert-base-tr-uncased This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [artiwise-ai/modernbert-base-tr-uncased](https://huggingface.co/artiwise-ai/modernbert-base-tr-uncased) on the [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. ## Model Details ### Model Description - **Model Type:** PyLate model - **Base model:** [artiwise-ai/modernbert-base-tr-uncased](https://huggingface.co/artiwise-ai/modernbert-base-tr-uncased) - **Document Length:** 180 tokens - **Query Length:** 32 tokens - **Output Dimensionality:** 128 tokens - **Similarity Function:** MaxSim - **Training Dataset:** - [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased) - **Language:** en ### Model Sources - **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/) - **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate) - **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate) ### Full Model Architecture ``` ColBERT( (0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) ) ``` ## Usage First install the PyLate library: ```bash pip install -U pylate ``` ### Retrieval PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval. #### Indexing documents First, load the ColBERT model and initialize the Voyager index, then encode and index your documents: ```python from pylate import indexes, models, retrieve # Step 1: Load the ColBERT model model = models.ColBERT( model_name_or_path=pylate_model_id, ) # Step 2: Initialize the Voyager index index = indexes.Voyager( index_folder="pylate-index", index_name="index", override=True, # This overwrites the existing index if any ) # Step 3: Encode the documents documents_ids = ["1", "2", "3"] documents = ["document 1 text", "document 2 text", "document 3 text"] documents_embeddings = model.encode( documents, batch_size=32, is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries show_progress_bar=True, ) # Step 4: Add document embeddings to the index by providing embeddings and corresponding ids index.add_documents( documents_ids=documents_ids, documents_embeddings=documents_embeddings, ) ``` Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: ```python # To load an index, simply instantiate it with the correct folder/name and without overriding it index = indexes.Voyager( index_folder="pylate-index", index_name="index", ) ``` #### Retrieving top-k documents for queries Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores: ```python # Step 1: Initialize the ColBERT retriever retriever = retrieve.ColBERT(index=index) # Step 2: Encode the queries queries_embeddings = model.encode( ["query for document 3", "query for document 1"], batch_size=32, is_query=True, # # Ensure that it is set to False to indicate that these are queries show_progress_bar=True, ) # Step 3: Retrieve top-k documents scores = retriever.retrieve( queries_embeddings=queries_embeddings, k=10, # Retrieve the top 10 matches for each query ) ``` ### Reranking If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank: ```python from pylate import rank, models queries = [ "query A", "query B", ] documents = [ ["document A", "document B"], ["document 1", "document C", "document B"], ] documents_ids = [ [1, 2], [1, 3, 2], ] model = models.ColBERT( model_name_or_path=pylate_model_id, ) queries_embeddings = model.encode( queries, is_query=True, ) documents_embeddings = model.encode( documents, is_query=False, ) reranked_documents = rank.rerank( documents_ids=documents_ids, queries_embeddings=queries_embeddings, documents_embeddings=documents_embeddings, ) ``` ## Evaluation ### Metrics #### Py Late Information Retrieval * Dataset: `['NanoDBPedia', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNQ', 'NanoSCIDOCS']` * Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator | Metric | NanoDBPedia | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNQ | NanoSCIDOCS | |:--------------------|:------------|:-------------|:-------------|:------------|:-----------|:------------| | MaxSim_accuracy@1 | 0.78 | 0.48 | 0.92 | 0.42 | 0.58 | 0.42 | | MaxSim_accuracy@3 | 0.92 | 0.62 | 0.98 | 0.6 | 0.7 | 0.62 | | MaxSim_accuracy@5 | 0.96 | 0.72 | 1.0 | 0.7 | 0.76 | 0.66 | | MaxSim_accuracy@10 | 1.0 | 0.72 | 1.0 | 0.8 | 0.84 | 0.78 | | MaxSim_precision@1 | 0.78 | 0.48 | 0.92 | 0.42 | 0.58 | 0.42 | | MaxSim_precision@3 | 0.68 | 0.28 | 0.5133 | 0.2 | 0.24 | 0.2933 | | MaxSim_precision@5 | 0.596 | 0.22 | 0.336 | 0.14 | 0.156 | 0.232 | | MaxSim_precision@10 | 0.546 | 0.13 | 0.17 | 0.08 | 0.09 | 0.158 | | MaxSim_recall@1 | 0.0808 | 0.2526 | 0.46 | 0.42 | 0.57 | 0.0887 | | MaxSim_recall@3 | 0.1904 | 0.3991 | 0.77 | 0.6 | 0.69 | 0.1817 | | MaxSim_recall@5 | 0.2626 | 0.5106 | 0.84 | 0.7 | 0.73 | 0.2397 | | MaxSim_recall@10 | 0.3926 | 0.5472 | 0.85 | 0.8 | 0.81 | 0.3247 | | **MaxSim_ndcg@10** | **0.6694** | **0.4799** | **0.834** | **0.6031** | **0.6919** | **0.3236** | | MaxSim_mrr@10 | 0.8612 | 0.562 | 0.9517 | 0.5408 | 0.6584 | 0.5338 | | MaxSim_map@100 | 0.5271 | 0.4136 | 0.7775 | 0.5487 | 0.6541 | 0.2443 | #### Pylate Custom Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with pylate_nano_beir_evaluator.PylateCustomNanoBEIREvaluator | Metric | Value | |:--------------------|:-----------| | MaxSim_accuracy@1 | 0.6 | | MaxSim_accuracy@3 | 0.74 | | MaxSim_accuracy@5 | 0.8 | | MaxSim_accuracy@10 | 0.8567 | | MaxSim_precision@1 | 0.6 | | MaxSim_precision@3 | 0.3678 | | MaxSim_precision@5 | 0.28 | | MaxSim_precision@10 | 0.1957 | | MaxSim_recall@1 | 0.312 | | MaxSim_recall@3 | 0.4719 | | MaxSim_recall@5 | 0.5471 | | MaxSim_recall@10 | 0.6207 | | **MaxSim_ndcg@10** | **0.6003** | | MaxSim_mrr@10 | 0.6846 | | MaxSim_map@100 | 0.5275 | ## Training Details ### Training Dataset #### train * Dataset: [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased) at [bd034f5](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge-tr-uncased/tree/bd034f56291b3b7a7dcde55ab0bd933977cc233e) * Size: 443,147 training samples * Columns: query_id, document_ids, and scores * Approximate statistics based on the first 1000 samples: | | query_id | document_ids | scores | |:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------| | type | string | list | list | | details | | | | * Samples: | query_id | document_ids | scores | |:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------| | 817836 | ['2716076', '6741935', '2681109', '5562684', '3507339', ...] | [1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...] | | 1045170 | ['5088671', '2953295', '8783471', '4268439', '6339935', ...] | [1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...] | | 1069432 | ['3724008', '314949', '8657336', '7420456', '879004', ...] | [1.0, 0.3706032931804657, 0.3508036434650421, 0.2823200523853302, 0.17563475668430328, ...] | * Loss: pylate.losses.distillation.Distillation ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `gradient_accumulation_steps`: 2 - `learning_rate`: 3e-05 - `num_train_epochs`: 1 - `bf16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 3e-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`: 0.0 - `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`: 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`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | NanoDBPedia_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 | |:------:|:-----:|:-------------:|:--------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------:|:--------------------------:|:----------------------------:| | 0.0036 | 100 | 0.0649 | - | - | - | - | - | - | - | | 0.0072 | 200 | 0.0559 | - | - | - | - | - | - | - | | 0.0108 | 300 | 0.0518 | - | - | - | - | - | - | - | | 0.0144 | 400 | 0.051 | - | - | - | - | - | - | - | | 0.0181 | 500 | 0.0492 | 0.6421 | 0.3808 | 0.7993 | 0.5565 | 0.5826 | 0.3050 | 0.5444 | | 0.0217 | 600 | 0.0467 | - | - | - | - | - | - | - | | 0.0253 | 700 | 0.0451 | - | - | - | - | - | - | - | | 0.0289 | 800 | 0.0443 | - | - | - | - | - | - | - | | 0.0325 | 900 | 0.0443 | - | - | - | - | - | - | - | | 0.0361 | 1000 | 0.0437 | 0.6449 | 0.4015 | 0.8003 | 0.5437 | 0.6092 | 0.3134 | 0.5522 | | 0.0397 | 1100 | 0.0433 | - | - | - | - | - | - | - | | 0.0433 | 1200 | 0.0427 | - | - | - | - | - | - | - | | 0.0469 | 1300 | 0.0414 | - | - | - | - | - | - | - | | 0.0505 | 1400 | 0.0417 | - | - | - | - | - | - | - | | 0.0542 | 1500 | 0.0418 | 0.6412 | 0.4285 | 0.8154 | 0.5866 | 0.6181 | 0.3219 | 0.5686 | | 0.0578 | 1600 | 0.0404 | - | - | - | - | - | - | - | | 0.0614 | 1700 | 0.0417 | - | - | - | - | - | - | - | | 0.0650 | 1800 | 0.0407 | - | - | - | - | - | - | - | | 0.0686 | 1900 | 0.0398 | - | - | - | - | - | - | - | | 0.0722 | 2000 | 0.0401 | 0.6499 | 0.4354 | 0.8150 | 0.5610 | 0.6445 | 0.3152 | 0.5702 | | 0.0758 | 2100 | 0.0404 | - | - | - | - | - | - | - | | 0.0794 | 2200 | 0.0395 | - | - | - | - | - | - | - | | 0.0830 | 2300 | 0.0404 | - | - | - | - | - | - | - | | 0.0867 | 2400 | 0.0393 | - | - | - | - | - | - | - | | 0.0903 | 2500 | 0.0387 | 0.6571 | 0.4435 | 0.8112 | 0.5786 | 0.6809 | 0.3232 | 0.5824 | | 0.0939 | 2600 | 0.0397 | - | - | - | - | - | - | - | | 0.0975 | 2700 | 0.0393 | - | - | - | - | - | - | - | | 0.1011 | 2800 | 0.0384 | - | - | - | - | - | - | - | | 0.1047 | 2900 | 0.0382 | - | - | - | - | - | - | - | | 0.1083 | 3000 | 0.0381 | 0.6437 | 0.4751 | 0.8175 | 0.5711 | 0.6422 | 0.3203 | 0.5783 | | 0.1119 | 3100 | 0.0382 | - | - | - | - | - | - | - | | 0.1155 | 3200 | 0.0381 | - | - | - | - | - | - | - | | 0.1191 | 3300 | 0.0385 | - | - | - | - | - | - | - | | 0.1228 | 3400 | 0.0374 | - | - | - | - | - | - | - | | 0.1264 | 3500 | 0.0382 | 0.6437 | 0.4833 | 0.8282 | 0.5955 | 0.6436 | 0.3190 | 0.5856 | | 0.1300 | 3600 | 0.0365 | - | - | - | - | - | - | - | | 0.1336 | 3700 | 0.0379 | - | - | - | - | - | - | - | | 0.1372 | 3800 | 0.0376 | - | - | - | - | - | - | - | | 0.1408 | 3900 | 0.0376 | - | - | - | - | - | - | - | | 0.1444 | 4000 | 0.0378 | 0.6511 | 0.4760 | 0.8151 | 0.5806 | 0.6874 | 0.3140 | 0.5874 | | 0.1480 | 4100 | 0.0365 | - | - | - | - | - | - | - | | 0.1516 | 4200 | 0.0362 | - | - | - | - | - | - | - | | 0.1553 | 4300 | 0.0374 | - | - | - | - | - | - | - | | 0.1589 | 4400 | 0.0359 | - | - | - | - | - | - | - | | 0.1625 | 4500 | 0.0368 | 0.6530 | 0.4458 | 0.8122 | 0.6101 | 0.6896 | 0.3174 | 0.5880 | | 0.1661 | 4600 | 0.0356 | - | - | - | - | - | - | - | | 0.1697 | 4700 | 0.0364 | - | - | - | - | - | - | - | | 0.1733 | 4800 | 0.0352 | - | - | - | - | - | - | - | | 0.1769 | 4900 | 0.0357 | - | - | - | - | - | - | - | | 0.1805 | 5000 | 0.0366 | 0.6611 | 0.4680 | 0.8152 | 0.6260 | 0.6715 | 0.3252 | 0.5945 | | 0.1841 | 5100 | 0.0358 | - | - | - | - | - | - | - | | 0.1877 | 5200 | 0.0366 | - | - | - | - | - | - | - | | 0.1914 | 5300 | 0.0348 | - | - | - | - | - | - | - | | 0.1950 | 5400 | 0.036 | - | - | - | - | - | - | - | | 0.1986 | 5500 | 0.0337 | 0.6595 | 0.4823 | 0.8162 | 0.6241 | 0.6620 | 0.3216 | 0.5943 | | 0.2022 | 5600 | 0.0347 | - | - | - | - | - | - | - | | 0.2058 | 5700 | 0.0361 | - | - | - | - | - | - | - | | 0.2094 | 5800 | 0.0356 | - | - | - | - | - | - | - | | 0.2130 | 5900 | 0.0359 | - | - | - | - | - | - | - | | 0.2166 | 6000 | 0.0359 | 0.6560 | 0.4820 | 0.8121 | 0.6457 | 0.6587 | 0.3181 | 0.5954 | | 0.2202 | 6100 | 0.0347 | - | - | - | - | - | - | - | | 0.2239 | 6200 | 0.0355 | - | - | - | - | - | - | - | | 0.2275 | 6300 | 0.0356 | - | - | - | - | - | - | - | | 0.2311 | 6400 | 0.0351 | - | - | - | - | - | - | - | | 0.2347 | 6500 | 0.0351 | 0.6650 | 0.4658 | 0.8291 | 0.6167 | 0.6742 | 0.3146 | 0.5942 | | 0.2383 | 6600 | 0.0361 | - | - | - | - | - | - | - | | 0.2419 | 6700 | 0.0352 | - | - | - | - | - | - | - | | 0.2455 | 6800 | 0.0358 | - | - | - | - | - | - | - | | 0.2491 | 6900 | 0.0339 | - | - | - | - | - | - | - | | 0.2527 | 7000 | 0.0345 | 0.6600 | 0.4700 | 0.8413 | 0.6449 | 0.6862 | 0.3163 | 0.6031 | | 0.2563 | 7100 | 0.0347 | - | - | - | - | - | - | - | | 0.2600 | 7200 | 0.0346 | - | - | - | - | - | - | - | | 0.2636 | 7300 | 0.0342 | - | - | - | - | - | - | - | | 0.2672 | 7400 | 0.0346 | - | - | - | - | - | - | - | | 0.2708 | 7500 | 0.0339 | 0.6583 | 0.4792 | 0.8295 | 0.6257 | 0.6788 | 0.3204 | 0.5986 | | 0.2744 | 7600 | 0.0344 | - | - | - | - | - | - | - | | 0.2780 | 7700 | 0.0323 | - | - | - | - | - | - | - | | 0.2816 | 7800 | 0.0333 | - | - | - | - | - | - | - | | 0.2852 | 7900 | 0.0334 | - | - | - | - | - | - | - | | 0.2888 | 8000 | 0.0333 | 0.6633 | 0.4660 | 0.8257 | 0.6251 | 0.6847 | 0.3229 | 0.5979 | | 0.2925 | 8100 | 0.0337 | - | - | - | - | - | - | - | | 0.2961 | 8200 | 0.0339 | - | - | - | - | - | - | - | | 0.2997 | 8300 | 0.0332 | - | - | - | - | - | - | - | | 0.3033 | 8400 | 0.0334 | - | - | - | - | - | - | - | | 0.3069 | 8500 | 0.0334 | 0.6744 | 0.4791 | 0.8204 | 0.6139 | 0.6654 | 0.3130 | 0.5944 | | 0.3105 | 8600 | 0.032 | - | - | - | - | - | - | - | | 0.3141 | 8700 | 0.0342 | - | - | - | - | - | - | - | | 0.3177 | 8800 | 0.0337 | - | - | - | - | - | - | - | | 0.3213 | 8900 | 0.0343 | - | - | - | - | - | - | - | | 0.3249 | 9000 | 0.0342 | 0.6643 | 0.4395 | 0.8270 | 0.6252 | 0.6828 | 0.3146 | 0.5922 | | 0.3286 | 9100 | 0.0332 | - | - | - | - | - | - | - | | 0.3322 | 9200 | 0.0337 | - | - | - | - | - | - | - | | 0.3358 | 9300 | 0.033 | - | - | - | - | - | - | - | | 0.3394 | 9400 | 0.0327 | - | - | - | - | - | - | - | | 0.3430 | 9500 | 0.0332 | 0.6676 | 0.4530 | 0.8400 | 0.6220 | 0.6753 | 0.3139 | 0.5953 | | 0.3466 | 9600 | 0.0315 | - | - | - | - | - | - | - | | 0.3502 | 9700 | 0.033 | - | - | - | - | - | - | - | | 0.3538 | 9800 | 0.0331 | - | - | - | - | - | - | - | | 0.3574 | 9900 | 0.0341 | - | - | - | - | - | - | - | | 0.3610 | 10000 | 0.0327 | 0.6602 | 0.4887 | 0.8308 | 0.6267 | 0.6806 | 0.3241 | 0.6018 | | 0.3647 | 10100 | 0.0338 | - | - | - | - | - | - | - | | 0.3683 | 10200 | 0.0327 | - | - | - | - | - | - | - | | 0.3719 | 10300 | 0.0325 | - | - | - | - | - | - | - | | 0.3755 | 10400 | 0.0342 | - | - | - | - | - | - | - | | 0.3791 | 10500 | 0.034 | 0.6659 | 0.4723 | 0.8313 | 0.6156 | 0.6803 | 0.3240 | 0.5982 | | 0.3827 | 10600 | 0.0323 | - | - | - | - | - | - | - | | 0.3863 | 10700 | 0.0329 | - | - | - | - | - | - | - | | 0.3899 | 10800 | 0.0328 | - | - | - | - | - | - | - | | 0.3935 | 10900 | 0.0324 | - | - | - | - | - | - | - | | 0.3972 | 11000 | 0.0321 | 0.6628 | 0.4937 | 0.8340 | 0.6373 | 0.6945 | 0.3268 | 0.6082 | | 0.4008 | 11100 | 0.0329 | - | - | - | - | - | - | - | | 0.4044 | 11200 | 0.0329 | - | - | - | - | - | - | - | | 0.4080 | 11300 | 0.0325 | - | - | - | - | - | - | - | | 0.4116 | 11400 | 0.0321 | - | - | - | - | - | - | - | | 0.4152 | 11500 | 0.0325 | 0.6617 | 0.4698 | 0.8419 | 0.6231 | 0.6853 | 0.3191 | 0.6002 | | 0.4188 | 11600 | 0.0327 | - | - | - | - | - | - | - | | 0.4224 | 11700 | 0.0327 | - | - | - | - | - | - | - | | 0.4260 | 11800 | 0.0326 | - | - | - | - | - | - | - | | 0.4296 | 11900 | 0.0329 | - | - | - | - | - | - | - | | 0.4333 | 12000 | 0.0332 | 0.6559 | 0.4860 | 0.8324 | 0.6160 | 0.6966 | 0.3219 | 0.6015 | | 0.4369 | 12100 | 0.0323 | - | - | - | - | - | - | - | | 0.4405 | 12200 | 0.0327 | - | - | - | - | - | - | - | | 0.4441 | 12300 | 0.0321 | - | - | - | - | - | - | - | | 0.4477 | 12400 | 0.0321 | - | - | - | - | - | - | - | | 0.4513 | 12500 | 0.0319 | 0.6630 | 0.4877 | 0.8310 | 0.6197 | 0.6943 | 0.3296 | 0.6042 | | 0.4549 | 12600 | 0.0326 | - | - | - | - | - | - | - | | 0.4585 | 12700 | 0.032 | - | - | - | - | - | - | - | | 0.4621 | 12800 | 0.032 | - | - | - | - | - | - | - | | 0.4658 | 12900 | 0.0302 | - | - | - | - | - | - | - | | 0.4694 | 13000 | 0.0311 | 0.6687 | 0.4726 | 0.8305 | 0.6191 | 0.6929 | 0.3233 | 0.6012 | | 0.4730 | 13100 | 0.0321 | - | - | - | - | - | - | - | | 0.4766 | 13200 | 0.0318 | - | - | - | - | - | - | - | | 0.4802 | 13300 | 0.032 | - | - | - | - | - | - | - | | 0.4838 | 13400 | 0.0315 | - | - | - | - | - | - | - | | 0.4874 | 13500 | 0.0317 | 0.6628 | 0.4781 | 0.8257 | 0.6153 | 0.6795 | 0.3172 | 0.5964 | | 0.4910 | 13600 | 0.0316 | - | - | - | - | - | - | - | | 0.4946 | 13700 | 0.0335 | - | - | - | - | - | - | - | | 0.4982 | 13800 | 0.0313 | - | - | - | - | - | - | - | | 0.5019 | 13900 | 0.0317 | - | - | - | - | - | - | - | | 0.5055 | 14000 | 0.0321 | 0.6579 | 0.4676 | 0.8351 | 0.6088 | 0.6774 | 0.3211 | 0.5946 | | 0.5091 | 14100 | 0.0318 | - | - | - | - | - | - | - | | 0.5127 | 14200 | 0.0328 | - | - | - | - | - | - | - | | 0.5163 | 14300 | 0.0307 | - | - | - | - | - | - | - | | 0.5199 | 14400 | 0.0326 | - | - | - | - | - | - | - | | 0.5235 | 14500 | 0.0322 | 0.6558 | 0.5042 | 0.8344 | 0.6093 | 0.6963 | 0.3244 | 0.6041 | | 0.5271 | 14600 | 0.0321 | - | - | - | - | - | - | - | | 0.5307 | 14700 | 0.0308 | - | - | - | - | - | - | - | | 0.5344 | 14800 | 0.0315 | - | - | - | - | - | - | - | | 0.5380 | 14900 | 0.0324 | - | - | - | - | - | - | - | | 0.5416 | 15000 | 0.0305 | 0.6598 | 0.4898 | 0.8402 | 0.6081 | 0.6945 | 0.3207 | 0.6022 | | 0.5452 | 15100 | 0.0324 | - | - | - | - | - | - | - | | 0.5488 | 15200 | 0.0315 | - | - | - | - | - | - | - | | 0.5524 | 15300 | 0.0311 | - | - | - | - | - | - | - | | 0.5560 | 15400 | 0.0317 | - | - | - | - | - | - | - | | 0.5596 | 15500 | 0.0309 | 0.6541 | 0.4770 | 0.8309 | 0.6234 | 0.6946 | 0.3282 | 0.6014 | | 0.5632 | 15600 | 0.0322 | - | - | - | - | - | - | - | | 0.5668 | 15700 | 0.0314 | - | - | - | - | - | - | - | | 0.5705 | 15800 | 0.0312 | - | - | - | - | - | - | - | | 0.5741 | 15900 | 0.0301 | - | - | - | - | - | - | - | | 0.5777 | 16000 | 0.0316 | 0.6699 | 0.4869 | 0.8348 | 0.6061 | 0.7020 | 0.3182 | 0.6030 | | 0.5813 | 16100 | 0.0309 | - | - | - | - | - | - | - | | 0.5849 | 16200 | 0.0297 | - | - | - | - | - | - | - | | 0.5885 | 16300 | 0.0319 | - | - | - | - | - | - | - | | 0.5921 | 16400 | 0.0305 | - | - | - | - | - | - | - | | 0.5957 | 16500 | 0.0309 | 0.6725 | 0.4863 | 0.8270 | 0.6131 | 0.6957 | 0.3254 | 0.6033 | | 0.5993 | 16600 | 0.0312 | - | - | - | - | - | - | - | | 0.6030 | 16700 | 0.0305 | - | - | - | - | - | - | - | | 0.6066 | 16800 | 0.0306 | - | - | - | - | - | - | - | | 0.6102 | 16900 | 0.0314 | - | - | - | - | - | - | - | | 0.6138 | 17000 | 0.0308 | 0.6720 | 0.4886 | 0.8269 | 0.6115 | 0.6809 | 0.3239 | 0.6006 | | 0.6174 | 17100 | 0.0307 | - | - | - | - | - | - | - | | 0.6210 | 17200 | 0.03 | - | - | - | - | - | - | - | | 0.6246 | 17300 | 0.0315 | - | - | - | - | - | - | - | | 0.6282 | 17400 | 0.0304 | - | - | - | - | - | - | - | | 0.6318 | 17500 | 0.0313 | 0.6646 | 0.4817 | 0.8216 | 0.6176 | 0.6967 | 0.3257 | 0.6013 | | 0.6354 | 17600 | 0.03 | - | - | - | - | - | - | - | | 0.6391 | 17700 | 0.0323 | - | - | - | - | - | - | - | | 0.6427 | 17800 | 0.0311 | - | - | - | - | - | - | - | | 0.6463 | 17900 | 0.0295 | - | - | - | - | - | - | - | | 0.6499 | 18000 | 0.0307 | 0.6726 | 0.4799 | 0.8249 | 0.6299 | 0.6865 | 0.3242 | 0.6030 | | 0.6535 | 18100 | 0.0313 | - | - | - | - | - | - | - | | 0.6571 | 18200 | 0.0299 | - | - | - | - | - | - | - | | 0.6607 | 18300 | 0.0303 | - | - | - | - | - | - | - | | 0.6643 | 18400 | 0.03 | - | - | - | - | - | - | - | | 0.6679 | 18500 | 0.0298 | 0.6694 | 0.4799 | 0.8340 | 0.6031 | 0.6919 | 0.3236 | 0.6003 |
### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.0.2 - PyLate: 1.2.0 - Transformers: 4.48.2 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.6.0 - 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" } ``` #### PyLate ```bibtex @misc{PyLate, title={PyLate: Flexible Training and Retrieval for Late Interaction Models}, author={Chaffin, Antoine and Sourty, Raphaël}, url={https://github.com/lightonai/pylate}, year={2024} } ```