--- language: - en tags: - sentence-transformers - cross-encoder - reranker - generated_from_trainer - dataset_size:10000 - loss:MSELoss datasets: - sentence-transformers/msmarco pipeline_tag: text-ranking library_name: sentence-transformers metrics: - map - mrr@10 - ndcg@10 model-index: - name: CrossEncoder results: - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoMSMARCO R100 type: NanoMSMARCO_R100 metrics: - type: map value: 0.0579 name: Map - type: mrr@10 value: 0.0329 name: Mrr@10 - type: ndcg@10 value: 0.0479 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNFCorpus R100 type: NanoNFCorpus_R100 metrics: - type: map value: 0.2867 name: Map - type: mrr@10 value: 0.4222 name: Mrr@10 - type: ndcg@10 value: 0.2546 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNQ R100 type: NanoNQ_R100 metrics: - type: map value: 0.0326 name: Map - type: mrr@10 value: 0.01 name: Mrr@10 - type: ndcg@10 value: 0.0229 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: 0.1257 name: Map - type: mrr@10 value: 0.155 name: Mrr@10 - type: ndcg@10 value: 0.1084 name: Ndcg@10 --- # CrossEncoder This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model trained on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset 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 - **Maximum Sequence Length:** 512 tokens - **Number of Output Labels:** 1 label - **Training Dataset:** - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) - **Language:** en ### 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("kselight/123BERT") # Get scores for pairs of texts pairs = [ ['what is ivana trump', 'The need for an independent investigation. As it stands, all three men in charge of the investigations into the Trump campaign are Republicans, and two of the three are vociferous Trump allies. Burr, the third, also tied himself to Trump during his close 2016 reelection campaign.'], ["hogan's goat meaning", 'hoganâ\x80\x99s goat. The phrase like Hoganâ\x80\x99s goat refers to something that is faulty, messed up, or stinks like a goat. The phrase is a reference to R.F. Outcaultâ\x80\x99s seminal newspaper comic Hoganâ\x80\x99s Alley, which debuted in 1895. The title of the strip changed to The Yellow Kid the following year.'], ['who made tokyo ghoul', "Tokyo Ghoul (Japanese: æ\x9d±äº¬å\x96°ç¨®ï¼\x88ã\x83\x88ã\x83¼ã\x82\xadã\x83§ã\x83¼ã\x82°ã\x83¼ã\x83«ï¼\x89, Hepburn: TÅ\x8dkyÅ\x8d GÅ«ru) is a Japanese manga series by Sui Ishida. It was serialized in Shueisha's seinen manga magazine Weekly Young Jump between September 2011 and September 2014 and has been collected in fourteen tankÅ\x8dbon volumes as of August 2014."], ['neck of the scottie dog', 'Classical guitars. The classical guitar neck blank is relatively small compared to what is needed for construction. This is because a classical neck is constructed differently than most other neck designs. The heel of the neck is built up by stacking blocks of wood to achieve the necessary height.'], ['what does bicameral mean in government', 'Top 10 amazing movie makeup transformations. In government, bicameralism is the practice of having two legislative or parliamentary chambers. The relationship between the two chambers of a bicameral legislature can vary. In some cases, they have equal power, and in others, one chamber is clearly superior to the other. It is commonplace in most federal systems to have a bicameral legislature.'], ] scores = model.predict(pairs) print(scores.shape) # (5,) # Or rank different texts based on similarity to a single text ranks = model.rank( 'what is ivana trump', [ 'The need for an independent investigation. As it stands, all three men in charge of the investigations into the Trump campaign are Republicans, and two of the three are vociferous Trump allies. Burr, the third, also tied himself to Trump during his close 2016 reelection campaign.', 'hoganâ\x80\x99s goat. The phrase like Hoganâ\x80\x99s goat refers to something that is faulty, messed up, or stinks like a goat. The phrase is a reference to R.F. Outcaultâ\x80\x99s seminal newspaper comic Hoganâ\x80\x99s Alley, which debuted in 1895. The title of the strip changed to The Yellow Kid the following year.', "Tokyo Ghoul (Japanese: æ\x9d±äº¬å\x96°ç¨®ï¼\x88ã\x83\x88ã\x83¼ã\x82\xadã\x83§ã\x83¼ã\x82°ã\x83¼ã\x83«ï¼\x89, Hepburn: TÅ\x8dkyÅ\x8d GÅ«ru) is a Japanese manga series by Sui Ishida. It was serialized in Shueisha's seinen manga magazine Weekly Young Jump between September 2011 and September 2014 and has been collected in fourteen tankÅ\x8dbon volumes as of August 2014.", 'Classical guitars. The classical guitar neck blank is relatively small compared to what is needed for construction. This is because a classical neck is constructed differently than most other neck designs. The heel of the neck is built up by stacking blocks of wood to achieve the necessary height.', 'Top 10 amazing movie makeup transformations. In government, bicameralism is the practice of having two legislative or parliamentary chambers. The relationship between the two chambers of a bicameral legislature can vary. In some cases, they have equal power, and in others, one chamber is clearly superior to the other. It is commonplace in most federal systems to have a bicameral legislature.', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Evaluation ### Metrics #### 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 | 0.0579 (-0.4317) | 0.2867 (+0.0257) | 0.0326 (-0.3870) | | mrr@10 | 0.0329 (-0.4446) | 0.4222 (-0.0777) | 0.0100 (-0.4167) | | **ndcg@10** | **0.0479 (-0.4925)** | **0.2546 (-0.0705)** | **0.0229 (-0.4778)** | #### 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 | 0.1257 (-0.2643) | | mrr@10 | 0.1550 (-0.3130) | | **ndcg@10** | **0.1084 (-0.3469)** | ## Training Details ### Training Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 10,000 training samples * Columns: score, query, and passage * Approximate statistics based on the first 1000 samples: | | score | query | passage | |:--------|:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | type | float | string | string | | details | | | | * Samples: | score | query | passage | |:--------------------------------|:-------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 6.720487356185913 | modern definition of democracy | Links. A Short Definition of Democracy U.S. president Abraham Lincoln (1809-1865) defined democracy as: «Government of the people, by the people, for the people» Democracy is by far the most challenging form of government-both for politicians and for the people.The term democracy comes from the Greek language and means rule by the (simple) people. The so-called democracies in classical antiquity (Athens and Rome) represent precursors of modern democracies.Like modern democracy, they were created as a reaction to a concentration and abuse of power by the rulers.he term democracy comes from the Greek language and means rule by the (simple) people. The so-called democracies in classical antiquity (Athens and Rome) represent precursors of modern democracies. | | 1.6529417037963867 | is celexa and fluoxetine same | Celexa (citalopram hydrobromide) is a type of antidepressant called a selective serotonin reuptake inhibitor (SSRI) indicated for the treatment of depression. Celexa is available in generic form. Common side effects of Celexa include. constipation, nausea, diarrhea, upset stomach, decreased sexual desire, | | -9.121654828389486 | what are 2 examples of nonpoint pollution | Concept of pollution tax. All such measures are compensatory in nature and it is not called pollution tax. The concept of pollution tax is something different. It entails that instead of doing offsetting work by yourself wherever you hurt environment either willfully or without any intention you have to pay for it. | * Loss: [MSELoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#mseloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity" } ``` ### Evaluation Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 1,000 evaluation samples * Columns: score, query, and passage * Approximate statistics based on the first 1000 samples: | | score | query | passage | |:--------|:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | float | string | string | | details | | | | * Samples: | score | query | passage | |:---------------------------------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | -11.078993638356527 | what is ivana trump | The need for an independent investigation. As it stands, all three men in charge of the investigations into the Trump campaign are Republicans, and two of the three are vociferous Trump allies. Burr, the third, also tied himself to Trump during his close 2016 reelection campaign. | | 8.86651055018107 | hogan's goat meaning | hogan’s goat. The phrase like Hogan’s goat refers to something that is faulty, messed up, or stinks like a goat. The phrase is a reference to R.F. Outcault’s seminal newspaper comic Hogan’s Alley, which debuted in 1895. The title of the strip changed to The Yellow Kid the following year. | | 8.381712992986044 | who made tokyo ghoul | Tokyo Ghoul (Japanese: 東京喰種（トーキョーグール）, Hepburn: Tōkyō GÅ«ru) is a Japanese manga series by Sui Ishida. It was serialized in Shueisha's seinen manga magazine Weekly Young Jump between September 2011 and September 2014 and has been collected in fourteen tankōbon volumes as of August 2014. | * Loss: [MSELoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#mseloss) with these parameters: ```json { "activation_fn": "torch.nn.modules.linear.Identity" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 8e-06 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `seed`: 12 - `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`: 8e-06 - `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`: False - `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 - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 | |:------:|:----:|:-------------:|:------------------------:|:-------------------------:|:-------------------:|:--------------------------:| | -1 | -1 | - | 0.0479 (-0.4925) | 0.2546 (-0.0705) | 0.0229 (-0.4778) | 0.1084 (-0.3469) | | 0.0064 | 1 | 53.6175 | - | - | - | - | ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 5.1.1 - Transformers: 4.47.1 - PyTorch: 2.4.0+cu124 - Accelerate: 1.5.1 - Datasets: 3.3.2 - 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", } ```