| --- |
| 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) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 --> |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Number of Output Labels:** 1 label |
| <!-- - **Training Dataset:** Unknown --> |
| - **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': ...}, ...] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Cross Encoder Reranking |
|
|
| * Dataset: `gooaq-dev` |
| * Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](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 [<code>CrossEncoderRerankingEvaluator</code>](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 [<code>CrossEncoderNanoBEIREvaluator</code>](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)** | |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 578,402 training samples |
| * Columns: <code>question</code>, <code>answer</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | question | answer | label | |
| |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 20 characters</li><li>mean: 42.74 characters</li><li>max: 83 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 250.28 characters</li><li>max: 385 characters</li></ul> | <ul><li>0: ~82.30%</li><li>1: ~17.70%</li></ul> | |
| * Samples: |
| | question | answer | label | |
| |:----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>how to put your phone on do not disturb on iphone?</code> | <code>Go to Settings > Do Not Disturb to turn on Do Not Disturb manually or set a schedule. to turn it on or off.</code> | <code>1</code> | |
| | <code>how to put your phone on do not disturb on iphone?</code> | <code>This icon means that your iPhone's Do Not Disturb feature is enabled.</code> | <code>0</code> | |
| | <code>how to put your phone on do not disturb on iphone?</code> | <code>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.</code> | <code>0</code> | |
| * Loss: [<code>BinaryCrossEntropyLoss</code>](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 |
| <details><summary>Click to expand</summary> |
|
|
| - `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 |
|
|
| </details> |
|
|
| ### 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", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
