| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - sentence-transformers |
| - cross-encoder |
| - reranker |
| - generated_from_trainer |
| - dataset_size:198 |
| - 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: 0.25 |
| name: Map |
| - type: mrr@10 |
| value: 0.25 |
| name: Mrr@10 |
| - type: ndcg@10 |
| value: 0.4307 |
| name: Ndcg@10 |
| - task: |
| type: cross-encoder-reranking |
| name: Cross Encoder Reranking |
| dataset: |
| name: NanoMSMARCO R100 |
| type: NanoMSMARCO_R100 |
| metrics: |
| - type: map |
| value: 0.0358 |
| name: Map |
| - type: mrr@10 |
| value: 0.0109 |
| name: Mrr@10 |
| - type: ndcg@10 |
| value: 0.026 |
| name: Ndcg@10 |
| - task: |
| type: cross-encoder-reranking |
| name: Cross Encoder Reranking |
| dataset: |
| name: NanoNFCorpus R100 |
| type: NanoNFCorpus_R100 |
| metrics: |
| - type: map |
| value: 0.2903 |
| name: Map |
| - type: mrr@10 |
| value: 0.5069 |
| name: Mrr@10 |
| - type: ndcg@10 |
| value: 0.29 |
| name: Ndcg@10 |
| - task: |
| type: cross-encoder-reranking |
| name: Cross Encoder Reranking |
| dataset: |
| name: NanoNQ R100 |
| type: NanoNQ_R100 |
| metrics: |
| - type: map |
| value: 0.0379 |
| name: Map |
| - type: mrr@10 |
| value: 0.014 |
| name: Mrr@10 |
| - type: ndcg@10 |
| value: 0.0174 |
| 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: 0.1213 |
| name: Map |
| - type: mrr@10 |
| value: 0.1772 |
| name: Mrr@10 |
| - type: ndcg@10 |
| value: 0.1111 |
| 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/huggingface/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("chingackgook/reranker-ModernBERT-base-gooaq-bce") |
| # Get scores for pairs of texts |
| pairs = [ |
| ['how many days can i drive my car without mot?', "If your car fails its MOT you can only continue to drive it if the previous year's MOT is still valid - which might occur if you submitted the car for its test two weeks early. You can still drive it away from the testing centre or garage if no 'dangerous' problems were identified during the MOT."], |
| ['how many days can i drive my car without mot?', '["Open File Explorer\', go to \'This PC\'. Check the status of the network drive. [00:11]", \'An error occurred while reconnecting or mapping Drive letter Z: to Network folder. [00:36]\', \'You can do either connect the drive (Map network drive) or Disconnect Network drive. [01:01]\', \'Disconnect Network Drive by this way. [01:28]\']'], |
| ['what xbox 360 games are compatible with xbox 1?', "['0 day Attack on Earth.', '3D Ultra Minigolf.', 'A Kingdom for Keflings.', 'A World of Keflings.', 'Ace Combat 6: Fires of Liberation.', 'Aegis Wing.', 'Age of Booty.', 'Alan Wake (Tested by Digital Foundry)']"], |
| ['what xbox 360 games are compatible with xbox 1?', "['1) Computer and Information Systems Managers.', '2) Computer and Information Research Scientists.', '3) Computer Network Architects.', '4) Software Development Engineer.', '5) Software Developers.', '6) Information Security Analysts.', '8) Computer Systems Analysts.']"], |
| ['what does it mean when a guy asks for a picture of you?', 'He wants to confirm if he is talking to Priya or Angel Priya (I.e., if he is really talking to a girl or just a guy with fake profile) They are talking to you and want to see how you look. I found it normal but would say, be careful about whom do you share your picture with as they might misuse it. I hate this one.'], |
| ] |
| scores = model.predict(pairs) |
| print(scores.shape) |
| # (5,) |
| |
| # Or rank different texts based on similarity to a single text |
| ranks = model.rank( |
| 'how many days can i drive my car without mot?', |
| [ |
| "If your car fails its MOT you can only continue to drive it if the previous year's MOT is still valid - which might occur if you submitted the car for its test two weeks early. You can still drive it away from the testing centre or garage if no 'dangerous' problems were identified during the MOT.", |
| '["Open File Explorer\', go to \'This PC\'. Check the status of the network drive. [00:11]", \'An error occurred while reconnecting or mapping Drive letter Z: to Network folder. [00:36]\', \'You can do either connect the drive (Map network drive) or Disconnect Network drive. [01:01]\', \'Disconnect Network Drive by this way. [01:28]\']', |
| "['0 day Attack on Earth.', '3D Ultra Minigolf.', 'A Kingdom for Keflings.', 'A World of Keflings.', 'Ace Combat 6: Fires of Liberation.', 'Aegis Wing.', 'Age of Booty.', 'Alan Wake (Tested by Digital Foundry)']", |
| "['1) Computer and Information Systems Managers.', '2) Computer and Information Research Scientists.', '3) Computer Network Architects.', '4) Software Development Engineer.', '5) Software Developers.', '6) Information Security Analysts.', '8) Computer Systems Analysts.']", |
| 'He wants to confirm if he is talking to Priya or Angel Priya (I.e., if he is really talking to a girl or just a guy with fake profile) They are talking to you and want to see how you look. I found it normal but would say, be careful about whom do you share your picture with as they might misuse it. I hate this one.', |
| ] |
| ) |
| # [{'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 | 0.2500 (-0.7500) | |
| | mrr@10 | 0.2500 (-0.7500) | |
| | **ndcg@10** | **0.4307 (-0.5693)** | |
|
|
| #### 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 | 0.0358 (-0.4538) | 0.2903 (+0.0293) | 0.0379 (-0.3817) | |
| | mrr@10 | 0.0109 (-0.4666) | 0.5069 (+0.0070) | 0.0140 (-0.4127) | |
| | **ndcg@10** | **0.0260 (-0.5145)** | **0.2900 (-0.0350)** | **0.0174 (-0.4833)** | |
| |
| #### 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 | 0.1213 (-0.2688) | |
| | mrr@10 | 0.1772 (-0.2908) | |
| | **ndcg@10** | **0.1111 (-0.3443)** | |
|
|
| <!-- |
| ## 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: 198 training samples |
| * Columns: <code>question</code>, <code>answer</code>, and <code>label</code> |
| * Approximate statistics based on the first 198 samples: |
| | | question | answer | label | |
| |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------| |
| | type | string | string | int | |
| | details | <ul><li>min: 21 characters</li><li>mean: 42.74 characters</li><li>max: 73 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 257.56 characters</li><li>max: 378 characters</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | |
| * Samples: |
| | question | answer | label | |
| |:-------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>how many days can i drive my car without mot?</code> | <code>If your car fails its MOT you can only continue to drive it if the previous year's MOT is still valid - which might occur if you submitted the car for its test two weeks early. You can still drive it away from the testing centre or garage if no 'dangerous' problems were identified during the MOT.</code> | <code>1</code> | |
| | <code>how many days can i drive my car without mot?</code> | <code>["Open File Explorer', go to 'This PC'. Check the status of the network drive. [00:11]", 'An error occurred while reconnecting or mapping Drive letter Z: to Network folder. [00:36]', 'You can do either connect the drive (Map network drive) or Disconnect Network drive. [01:01]', 'Disconnect Network Drive by this way. [01:28]']</code> | <code>0</code> | |
| | <code>what xbox 360 games are compatible with xbox 1?</code> | <code>['0 day Attack on Earth.', '3D Ultra Minigolf.', 'A Kingdom for Keflings.', 'A World of Keflings.', 'Ace Combat 6: Fires of Liberation.', 'Aegis Wing.', 'Age of Booty.', 'Alan Wake (Tested by Digital Foundry)']</code> | <code>1</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": 1 |
| } |
| ``` |
|
|
| ### 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 |
| - `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} |
| - `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} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `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`: no |
| - `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`: True |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </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 | - | 0.4307 (-0.5693) | 0.0220 (-0.5184) | 0.2802 (-0.0448) | 0.0200 (-0.4806) | 0.1074 (-0.3479) | |
| | 0.25 | 1 | 0.7171 | - | - | - | - | - | |
| | -1 | -1 | - | 0.4307 (-0.5693) | 0.0260 (-0.5145) | 0.2900 (-0.0350) | 0.0174 (-0.4833) | 0.1111 (-0.3443) | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.10.19 |
| - Sentence Transformers: 5.2.0.dev0 |
| - Transformers: 4.57.3 |
| - PyTorch: 2.9.1+cu128 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.4.1 |
| - Tokenizers: 0.22.1 |
|
|
| ## 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.* |
| --> |
