--- 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: 0.7258 name: Map - type: mrr@10 value: 0.7245 name: Mrr@10 - type: ndcg@10 value: 0.7686 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoMSMARCO R100 type: NanoMSMARCO_R100 metrics: - type: map value: 0.4807 name: Map - type: mrr@10 value: 0.4689 name: Mrr@10 - type: ndcg@10 value: 0.5499 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNFCorpus R100 type: NanoNFCorpus_R100 metrics: - type: map value: 0.3866 name: Map - type: mrr@10 value: 0.6058 name: Mrr@10 - type: ndcg@10 value: 0.4233 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNQ R100 type: NanoNQ_R100 metrics: - type: map value: 0.5595 name: Map - type: mrr@10 value: 0.5752 name: Mrr@10 - type: ndcg@10 value: 0.6191 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: 0.4756 name: Map - type: mrr@10 value: 0.55 name: Mrr@10 - type: ndcg@10 value: 0.5308 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("akr2002/reranker-ModernBERT-base-gooaq-bce") # Get scores for pairs of texts pairs = [ ['how do you find mass?', "Divide the object's weight by the acceleration of gravity to find the mass. You'll need to convert the weight units to Newtons. For example, 1 kg = 9.807 N. If you're measuring the mass of an object on Earth, divide the weight in Newtons by the acceleration of gravity on Earth (9.8 meters/second2) to get mass."], ['how do you find mass?', "In general use, 'High Mass' means a full ceremonial Mass, most likely with music, and also with incense if they're particularly traditional. ... Incense is used quite a lot. Low Mass in the traditional rite is celebrated by one priest, and usually only one or two altar servers."], ['how do you find mass?', 'A neutron has a slightly larger mass than the proton. These are often given in terms of an atomic mass unit, where one atomic mass unit (u) is defined as 1/12th the mass of a carbon-12 atom. You can use that to prove that a mass of 1 u is equivalent to an energy of 931.5 MeV.'], ['how do you find mass?', 'Mass is the amount of matter in a body, normally measured in grams or kilograms etc. Weight is a force that pulls on a mass and is measured in Newtons. ... Density basically means how much mass is occupied in a specific volume or space. Different materials of the same size may have different masses because of its density.'], ['how do you find mass?', 'Receiver – Mass communication is the transmission of the message to a large number of recipients. This mass of receivers, are often called as mass audience. The Mass audience is large, heterogenous and anonymous in nature. The receivers are scattered across a given village, state or country.'], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'how do you find mass?', [ "Divide the object's weight by the acceleration of gravity to find the mass. You'll need to convert the weight units to Newtons. For example, 1 kg = 9.807 N. If you're measuring the mass of an object on Earth, divide the weight in Newtons by the acceleration of gravity on Earth (9.8 meters/second2) to get mass.", "In general use, 'High Mass' means a full ceremonial Mass, most likely with music, and also with incense if they're particularly traditional. ... Incense is used quite a lot. Low Mass in the traditional rite is celebrated by one priest, and usually only one or two altar servers.", 'A neutron has a slightly larger mass than the proton. These are often given in terms of an atomic mass unit, where one atomic mass unit (u) is defined as 1/12th the mass of a carbon-12 atom. You can use that to prove that a mass of 1 u is equivalent to an energy of 931.5 MeV.', 'Mass is the amount of matter in a body, normally measured in grams or kilograms etc. Weight is a force that pulls on a mass and is measured in Newtons. ... Density basically means how much mass is occupied in a specific volume or space. Different materials of the same size may have different masses because of its density.', 'Receiver – Mass communication is the transmission of the message to a large number of recipients. This mass of receivers, are often called as mass audience. The Mass audience is large, heterogenous and anonymous in nature. The receivers are scattered across a given village, state or country.', ] ) # [{'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 | 0.7258 (+0.1946) | | mrr@10 | 0.7245 (+0.2005) | | **ndcg@10** | **0.7686 (+0.1774)** | #### 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 | 0.4807 (-0.0089) | 0.3866 (+0.1256) | 0.5595 (+0.1399) | | mrr@10 | 0.4689 (-0.0086) | 0.6058 (+0.1060) | 0.5752 (+0.1485) | | **ndcg@10** | **0.5499 (+0.0095)** | **0.4233 (+0.0982)** | **0.6191 (+0.1184)** | #### 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 | 0.4756 (+0.0855) | | mrr@10 | 0.5500 (+0.0820) | | **ndcg@10** | **0.5308 (+0.0754)** | ## 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 do you find mass? | Divide the object's weight by the acceleration of gravity to find the mass. You'll need to convert the weight units to Newtons. For example, 1 kg = 9.807 N. If you're measuring the mass of an object on Earth, divide the weight in Newtons by the acceleration of gravity on Earth (9.8 meters/second2) to get mass. | 1 | | how do you find mass? | In general use, 'High Mass' means a full ceremonial Mass, most likely with music, and also with incense if they're particularly traditional. ... Incense is used quite a lot. Low Mass in the traditional rite is celebrated by one priest, and usually only one or two altar servers. | 0 | | how do you find mass? | A neutron has a slightly larger mass than the proton. These are often given in terms of an atomic mass unit, where one atomic mass unit (u) is defined as 1/12th the mass of a carbon-12 atom. You can use that to prove that a mass of 1 u is equivalent to an energy of 931.5 MeV. | 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 | - | 0.1474 (-0.4438) | 0.0356 (-0.5048) | 0.2344 (-0.0907) | 0.0268 (-0.4739) | 0.0989 (-0.3564) | | 0.0000 | 1 | 1.1353 | - | - | - | - | - | | 0.0277 | 1000 | 1.1797 | - | - | - | - | - | | 0.0553 | 2000 | 0.8539 | - | - | - | - | - | | 0.0830 | 3000 | 0.7438 | - | - | - | - | - | | 0.1106 | 4000 | 0.7296 | 0.7119 (+0.1206) | 0.5700 (+0.0296) | 0.3410 (+0.0160) | 0.6012 (+0.1005) | 0.5041 (+0.0487) | | 0.1383 | 5000 | 0.6705 | - | - | - | - | - | | 0.1660 | 6000 | 0.6624 | - | - | - | - | - | | 0.1936 | 7000 | 0.6685 | - | - | - | - | - | | 0.2213 | 8000 | 0.6305 | 0.7328 (+0.1415) | 0.5504 (+0.0099) | 0.4056 (+0.0805) | 0.6947 (+0.1941) | 0.5502 (+0.0948) | | 0.2490 | 9000 | 0.6353 | - | - | - | - | - | | 0.2766 | 10000 | 0.6118 | - | - | - | - | - | | 0.3043 | 11000 | 0.6097 | - | - | - | - | - | | 0.3319 | 12000 | 0.6003 | 0.7423 (+0.1510) | 0.5817 (+0.0413) | 0.3817 (+0.0566) | 0.6152 (+0.1145) | 0.5262 (+0.0708) | | 0.3596 | 13000 | 0.5826 | - | - | - | - | - | | 0.3873 | 14000 | 0.5935 | - | - | - | - | - | | 0.4149 | 15000 | 0.5826 | - | - | - | - | - | | 0.4426 | 16000 | 0.5723 | 0.7557 (+0.1645) | 0.5453 (+0.0049) | 0.4029 (+0.0779) | 0.6260 (+0.1253) | 0.5247 (+0.0693) | | 0.4702 | 17000 | 0.582 | - | - | - | - | - | | 0.4979 | 18000 | 0.5631 | - | - | - | - | - | | 0.5256 | 19000 | 0.5705 | - | - | - | - | - | | 0.5532 | 20000 | 0.544 | 0.7604 (+0.1692) | 0.5636 (+0.0232) | 0.4112 (+0.0862) | 0.6260 (+0.1253) | 0.5336 (+0.0782) | | 0.5809 | 21000 | 0.5289 | - | - | - | - | - | | 0.6086 | 22000 | 0.5431 | - | - | - | - | - | | 0.6362 | 23000 | 0.5449 | - | - | - | - | - | | 0.6639 | 24000 | 0.5338 | 0.7608 (+0.1696) | 0.5384 (-0.0020) | 0.4327 (+0.1077) | 0.5906 (+0.0899) | 0.5206 (+0.0652) | | 0.6915 | 25000 | 0.5401 | - | - | - | - | - | | 0.7192 | 26000 | 0.5535 | - | - | - | - | - | | 0.7469 | 27000 | 0.5353 | - | - | - | - | - | | 0.7745 | 28000 | 0.5157 | 0.7635 (+0.1723) | 0.5217 (-0.0188) | 0.4171 (+0.0921) | 0.5543 (+0.0537) | 0.4977 (+0.0423) | | 0.8022 | 29000 | 0.5153 | - | - | - | - | - | | 0.8299 | 30000 | 0.5122 | - | - | - | - | - | | 0.8575 | 31000 | 0.5108 | - | - | - | - | - | | 0.8852 | 32000 | 0.5303 | 0.7685 (+0.1773) | 0.5538 (+0.0134) | 0.4147 (+0.0897) | 0.6155 (+0.1149) | 0.5280 (+0.0727) | | 0.9128 | 33000 | 0.5363 | - | - | - | - | - | | 0.9405 | 34000 | 0.4996 | - | - | - | - | - | | 0.9682 | 35000 | 0.5193 | - | - | - | - | - | | **0.9958** | **36000** | **0.4995** | **0.7686 (+0.1774)** | **0.5499 (+0.0095)** | **0.4233 (+0.0982)** | **0.6191 (+0.1184)** | **0.5308 (+0.0754)** | | -1 | -1 | - | 0.7686 (+0.1774) | 0.5499 (+0.0095) | 0.4233 (+0.0982) | 0.6191 (+0.1184) | 0.5308 (+0.0754) | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.7 - Sentence Transformers: 4.0.1 - Transformers: 4.50.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.5.0 - 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", } ```