--- 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: 0.7308 name: Map - type: mrr@10 value: 0.7292 name: Mrr@10 - type: ndcg@10 value: 0.7713 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoMSMARCO R100 type: NanoMSMARCO_R100 metrics: - type: map value: 0.4579 name: Map - type: mrr@10 value: 0.4479 name: Mrr@10 - type: ndcg@10 value: 0.5275 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNFCorpus R100 type: NanoNFCorpus_R100 metrics: - type: map value: 0.3414 name: Map - type: mrr@10 value: 0.534 name: Mrr@10 - type: ndcg@10 value: 0.3821 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNQ R100 type: NanoNQ_R100 metrics: - type: map value: 0.3932 name: Map - type: mrr@10 value: 0.3918 name: Mrr@10 - type: ndcg@10 value: 0.463 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: 0.3975 name: Map - type: mrr@10 value: 0.4579 name: Mrr@10 - type: ndcg@10 value: 0.4575 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. See [training_gooaq_bce.py](https://github.com/UKPLab/sentence-transformers/blob/feat/cross_encoder_trainer/examples/cross_encoder/training/rerankers/training_gooaq_bce.py) for the training script. This script is also described in the [Cross Encoder > Training Overview](https://sbert.net/docs/cross_encoder/training_overview.html) documentation and the [Training and Finetuning Reranker Models with Sentence Transformers v4](https://huggingface.co/blog/train-reranker) blogpost. ![Model size vs NDCG for Rerankers on GooAQ](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/train-reranker/reranker_gooaq_model_size_ndcg.png) ## 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("tomaarsen/reranker-ModernBERT-base-gooaq-bce") # Get scores for pairs of texts pairs = [ ['why are rye chips so good?', "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips."], ['why are rye chips so good?', 'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.'], ['why are rye chips so good?', 'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.'], ['why are rye chips so good?', 'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.'], ['why are rye chips so good?', 'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.'], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'why are rye chips so good?', [ "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips.", 'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.', 'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.', 'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.', 'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.', ] ) # [{'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 | 0.7308 (+0.1997) | | mrr@10 | 0.7292 (+0.2052) | | **ndcg@10** | **0.7713 (+0.1801)** | #### 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": true } ``` | Metric | Value | |:------------|:---------------------| | map | 0.7908 (+0.2597) | | mrr@10 | 0.7890 (+0.2650) | | **ndcg@10** | **0.8351 (+0.2439)** | #### 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 | 0.4579 (-0.0317) | 0.3414 (+0.0804) | 0.3932 (-0.0264) | | mrr@10 | 0.4479 (-0.0296) | 0.5340 (+0.0342) | 0.3918 (-0.0349) | | **ndcg@10** | **0.5275 (-0.0130)** | **0.3821 (+0.0571)** | **0.4630 (-0.0377)** | #### 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 | 0.3975 (+0.0074) | | mrr@10 | 0.4579 (-0.0101) | | **ndcg@10** | **0.4575 (+0.0022)** | ## 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 | |:----------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | why are rye chips so good? | It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips. | 1 | | why are rye chips so good? | There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads. | 0 | | why are rye chips so good? | Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains. | 0 | * Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "activation_fct": "torch.nn.modules.linear.Identity", "pos_weight": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `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`: 64 - `per_device_eval_batch_size`: 64 - `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} - `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 | - | 0.1288 (-0.4624) | 0.0149 (-0.5255) | 0.2278 (-0.0972) | 0.0229 (-0.4777) | 0.0885 (-0.3668) | | 0.0001 | 1 | 1.0435 | - | - | - | - | - | | 0.0221 | 200 | 1.1924 | - | - | - | - | - | | 0.0443 | 400 | 1.1531 | - | - | - | - | - | | 0.0664 | 600 | 0.9371 | - | - | - | - | - | | 0.0885 | 800 | 0.6993 | - | - | - | - | - | | 0.1106 | 1000 | 0.669 | 0.7042 (+0.1130) | 0.4353 (-0.1051) | 0.3289 (+0.0039) | 0.4250 (-0.0757) | 0.3964 (-0.0590) | | 0.1328 | 1200 | 0.6257 | - | - | - | - | - | | 0.1549 | 1400 | 0.6283 | - | - | - | - | - | | 0.1770 | 1600 | 0.6014 | - | - | - | - | - | | 0.1992 | 1800 | 0.5888 | - | - | - | - | - | | 0.2213 | 2000 | 0.5493 | 0.7425 (+0.1513) | 0.4947 (-0.0457) | 0.3568 (+0.0318) | 0.4634 (-0.0373) | 0.4383 (-0.0171) | | 0.2434 | 2200 | 0.5479 | - | - | - | - | - | | 0.2655 | 2400 | 0.5329 | - | - | - | - | - | | 0.2877 | 2600 | 0.5208 | - | - | - | - | - | | 0.3098 | 2800 | 0.5259 | - | - | - | - | - | | 0.3319 | 3000 | 0.5221 | 0.7479 (+0.1567) | 0.5146 (-0.0258) | 0.3710 (+0.0460) | 0.4846 (-0.0160) | 0.4568 (+0.0014) | | 0.3541 | 3200 | 0.4977 | - | - | - | - | - | | 0.3762 | 3400 | 0.4965 | - | - | - | - | - | | 0.3983 | 3600 | 0.4985 | - | - | - | - | - | | 0.4204 | 3800 | 0.4907 | - | - | - | - | - | | 0.4426 | 4000 | 0.5058 | 0.7624 (+0.1712) | 0.5166 (-0.0238) | 0.3665 (+0.0415) | 0.4868 (-0.0138) | 0.4567 (+0.0013) | | 0.4647 | 4200 | 0.4885 | - | - | - | - | - | | 0.4868 | 4400 | 0.495 | - | - | - | - | - | | 0.5090 | 4600 | 0.4839 | - | - | - | - | - | | 0.5311 | 4800 | 0.4983 | - | - | - | - | - | | 0.5532 | 5000 | 0.4778 | 0.7603 (+0.1691) | 0.5110 (-0.0294) | 0.3540 (+0.0290) | 0.4809 (-0.0197) | 0.4487 (-0.0067) | | 0.5753 | 5200 | 0.4726 | - | - | - | - | - | | 0.5975 | 5400 | 0.477 | - | - | - | - | - | | 0.6196 | 5600 | 0.4613 | - | - | - | - | - | | 0.6417 | 5800 | 0.4492 | - | - | - | - | - | | 0.6639 | 6000 | 0.4506 | 0.7643 (+0.1731) | 0.5275 (-0.0129) | 0.3639 (+0.0389) | 0.4913 (-0.0094) | 0.4609 (+0.0055) | | 0.6860 | 6200 | 0.4618 | - | - | - | - | - | | 0.7081 | 6400 | 0.463 | - | - | - | - | - | | 0.7303 | 6600 | 0.4585 | - | - | - | - | - | | 0.7524 | 6800 | 0.4612 | - | - | - | - | - | | 0.7745 | 7000 | 0.4621 | 0.7649 (+0.1736) | 0.5105 (-0.0299) | 0.3688 (+0.0437) | 0.4552 (-0.0454) | 0.4448 (-0.0105) | | 0.7966 | 7200 | 0.4536 | - | - | - | - | - | | 0.8188 | 7400 | 0.4515 | - | - | - | - | - | | 0.8409 | 7600 | 0.4396 | - | - | - | - | - | | 0.8630 | 7800 | 0.4542 | - | - | - | - | - | | 0.8852 | 8000 | 0.4332 | 0.7669 (+0.1757) | 0.5247 (-0.0157) | 0.3794 (+0.0544) | 0.4370 (-0.0637) | 0.4470 (-0.0083) | | 0.9073 | 8200 | 0.447 | - | - | - | - | - | | 0.9294 | 8400 | 0.4335 | - | - | - | - | - | | 0.9515 | 8600 | 0.4179 | - | - | - | - | - | | 0.9737 | 8800 | 0.4459 | - | - | - | - | - | | **0.9958** | **9000** | **0.4196** | **0.7713 (+0.1801)** | **0.5275 (-0.0130)** | **0.3821 (+0.0571)** | **0.4630 (-0.0377)** | **0.4575 (+0.0022)** | | -1 | -1 | - | 0.7713 (+0.1801) | 0.5275 (-0.0130) | 0.3821 (+0.0571) | 0.4630 (-0.0377) | 0.4575 (+0.0022) | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.5.0.dev0 - Transformers: 4.49.0 - PyTorch: 2.5.1+cu124 - Accelerate: 1.5.2 - Datasets: 2.21.0 - Tokenizers: 0.21.0 ## 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", } ```