--- language: - en license: apache-2.0 tags: - sentence-transformers - cross-encoder - generated_from_trainer - dataset_size:578402 - loss:BinaryCrossEntropyLoss base_model: answerdotai/ModernBERT-base pipeline_tag: text-ranking library_name: sentence-transformers metrics: - map - mrr@10 - ndcg@10 model-index: - name: ModernBERT-base trained on GooAQ results: - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: gooaq dev type: gooaq-dev metrics: - type: map value: 0.7246 name: Map - type: mrr@10 value: 0.7232 name: Mrr@10 - type: ndcg@10 value: 0.7671 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoMSMARCO R100 type: NanoMSMARCO_R100 metrics: - type: map value: 0.4258 name: Map - type: mrr@10 value: 0.4133 name: Mrr@10 - type: ndcg@10 value: 0.4863 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNFCorpus R100 type: NanoNFCorpus_R100 metrics: - type: map value: 0.3246 name: Map - type: mrr@10 value: 0.5233 name: Mrr@10 - type: ndcg@10 value: 0.3615 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNQ R100 type: NanoNQ_R100 metrics: - type: map value: 0.4195 name: Map - type: mrr@10 value: 0.4245 name: Mrr@10 - type: ndcg@10 value: 0.5073 name: Ndcg@10 - task: type: cross-encoder-nano-beir name: Cross Encoder Nano BEIR dataset: name: NanoBEIR R100 mean type: NanoBEIR_R100_mean metrics: - type: map value: 0.3899 name: Map - type: mrr@10 value: 0.4537 name: Mrr@10 - type: ndcg@10 value: 0.4517 name: Ndcg@10 --- # ModernBERT-base trained on GooAQ This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search. ## Model Details ### Model Description - **Model Type:** Cross Encoder - **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) - **Maximum Sequence Length:** 8192 tokens - **Number of Output Labels:** 1 label - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) ## 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 CrossEncoder # Download from the 🤗 Hub model = CrossEncoder("baseten-admin/reranker-ModernBERT-base-gooaq-bce") # Get scores for pairs of texts pairs = [ ['how to put your phone on do not disturb on iphone?', 'Go to Settings > Do Not Disturb to turn on Do Not Disturb manually or set a schedule. to turn it on or off.'], ['how to put your phone on do not disturb on iphone?', "This icon means that your iPhone's Do Not Disturb feature is enabled."], ['how to put your phone on do not disturb on iphone?', 'About Do Not Disturb The Do Not Disturb option on the iPhone stops notifications, alerts and calls from making any noise, vibration or lighting up the phone screen when the screen is locked.'], ['how to put your phone on do not disturb on iphone?', 'Go to Settings > Do Not Disturb to turn on Do Not Disturb manually or set a schedule. to turn it on or off. If you set an alarm in the Clock app, the alarm goes off even when Do Not Disturb is on. Learn how to set and manage your alarms.'], ['how to put your phone on do not disturb on iphone?', "You can use the Do Not Disturb feature on your iPhone whenever you want to block any calls, texts, or other notifications from making your phone ring. The notifications and alerts will still be stored on your phone, and you can check them at any time, but your iPhone won't light up or ring."], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'how to put your phone on do not disturb on iphone?', [ 'Go to Settings > Do Not Disturb to turn on Do Not Disturb manually or set a schedule. to turn it on or off.', "This icon means that your iPhone's Do Not Disturb feature is enabled.", 'About Do Not Disturb The Do Not Disturb option on the iPhone stops notifications, alerts and calls from making any noise, vibration or lighting up the phone screen when the screen is locked.', 'Go to Settings > Do Not Disturb to turn on Do Not Disturb manually or set a schedule. to turn it on or off. If you set an alarm in the Clock app, the alarm goes off even when Do Not Disturb is on. Learn how to set and manage your alarms.', "You can use the Do Not Disturb feature on your iPhone whenever you want to block any calls, texts, or other notifications from making your phone ring. The notifications and alerts will still be stored on your phone, and you can check them at any time, but your iPhone won't light up or ring.", ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Evaluation ### Metrics #### Cross Encoder Reranking * Dataset: `gooaq-dev` * Evaluated with [CrossEncoderRerankingEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: ```json { "at_k": 10, "always_rerank_positives": false } ``` | Metric | Value | |:------------|:---------------------| | map | 0.7246 (+0.1935) | | mrr@10 | 0.7232 (+0.1992) | | **ndcg@10** | **0.7671 (+0.1759)** | #### Cross Encoder Reranking * Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100` * Evaluated with [CrossEncoderRerankingEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: ```json { "at_k": 10, "always_rerank_positives": true } ``` | Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 | |:------------|:---------------------|:---------------------|:---------------------| | map | 0.4258 (-0.0638) | 0.3246 (+0.0636) | 0.4195 (-0.0001) | | mrr@10 | 0.4133 (-0.0642) | 0.5233 (+0.0235) | 0.4245 (-0.0022) | | **ndcg@10** | **0.4863 (-0.0541)** | **0.3615 (+0.0364)** | **0.5073 (+0.0067)** | #### Cross Encoder Nano BEIR * Dataset: `NanoBEIR_R100_mean` * Evaluated with [CrossEncoderNanoBEIREvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "rerank_k": 100, "at_k": 10, "always_rerank_positives": true } ``` | Metric | Value | |:------------|:---------------------| | map | 0.3899 (-0.0001) | | mrr@10 | 0.4537 (-0.0143) | | **ndcg@10** | **0.4517 (-0.0036)** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 578,402 training samples * Columns: question, answer, and label * Approximate statistics based on the first 1000 samples: | | question | answer | label | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | question | answer | label | |:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | how to put your phone on do not disturb on iphone? | Go to Settings > Do Not Disturb to turn on Do Not Disturb manually or set a schedule. to turn it on or off. | 1 | | how to put your phone on do not disturb on iphone? | This icon means that your iPhone's Do Not Disturb feature is enabled. | 0 | | how to put your phone on do not disturb on iphone? | About Do Not Disturb The Do Not Disturb option on the iPhone stops notifications, alerts and calls from making any noise, vibration or lighting up the phone screen when the screen is locked. | 0 | * Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity", "pos_weight": 5 } ``` ### 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 - `seed`: 12 - `bf16`: True - `dataloader_num_workers`: 4 - `load_best_model_at_end`: 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`: 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`: 12 - `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`: 4 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | gooaq-dev_ndcg@10 | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 | |:----------:|:--------:|:-------------:|:--------------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:| | -1 | -1 | - | 0.1394 (-0.4518) | 0.0204 (-0.5200) | 0.2531 (-0.0719) | 0.0693 (-0.4313) | 0.1143 (-0.3411) | | 0.0002 | 1 | 1.2794 | - | - | - | - | - | | 0.2213 | 1000 | 0.8021 | - | - | - | - | - | | 0.4426 | 2000 | 0.5164 | - | - | - | - | - | | 0.6639 | 3000 | 0.4769 | - | - | - | - | - | | **0.8852** | **4000** | **0.4613** | **0.7671 (+0.1759)** | **0.4863 (-0.0541)** | **0.3615 (+0.0364)** | **0.5073 (+0.0067)** | **0.4517 (-0.0036)** | | -1 | -1 | - | 0.7671 (+0.1759) | 0.4863 (-0.0541) | 0.3615 (+0.0364) | 0.5073 (+0.0067) | 0.4517 (-0.0036) | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.0.2 - Transformers: 4.50.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.4.1 - 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", } ```