--- language: - en tags: - sentence-transformers - cross-encoder - text-classification - generated_from_trainer - dataset_size:404290 - loss:BinaryCrossEntropyLoss base_model: distilbert/distilroberta-base datasets: - sentence-transformers/quora-duplicates pipeline_tag: text-classification library_name: sentence-transformers metrics: - accuracy - accuracy_threshold - f1 - f1_threshold - precision - recall - average_precision co2_eq_emissions: emissions: 26.889480385249758 energy_consumed: 0.06917762292257246 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.214 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: CrossEncoder based on distilbert/distilroberta-base results: - task: type: cross-encoder-classification name: Cross Encoder Classification dataset: name: quora duplicates dev type: quora-duplicates-dev metrics: - type: accuracy value: 0.8938 name: Accuracy - type: accuracy_threshold value: 0.5088549852371216 name: Accuracy Threshold - type: f1 value: 0.8612281373675477 name: F1 - type: f1_threshold value: 0.3856155276298523 name: F1 Threshold - type: precision value: 0.8182920912178554 name: Precision - type: recall value: 0.908919428725411 name: Recall - type: average_precision value: 0.920292628179356 name: Average Precision - task: type: cross-encoder-classification name: Cross Encoder Classification dataset: name: quora duplicates test type: quora-duplicates-test metrics: - type: accuracy value: 0.8938 name: Accuracy - type: accuracy_threshold value: 0.5091445446014404 name: Accuracy Threshold - type: f1 value: 0.8612281373675477 name: F1 - type: f1_threshold value: 0.38580775260925293 name: F1 Threshold - type: precision value: 0.8182920912178554 name: Precision - type: recall value: 0.908919428725411 name: Recall - type: average_precision value: 0.92029239602284 name: Average Precision --- # CrossEncoder based on distilbert/distilroberta-base This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Cross Encoder - **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) - **Maximum Sequence Length:** 514 tokens - **Training Dataset:** - [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) - **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("sentence_transformers_model_id") # Get scores for pairs... pairs = [ ['What is the step by step guide to invest in share market in india?', 'What is the step by step guide to invest in share market?'], ['What is the story of Kohinoor (Koh-i-Noor) Diamond?', 'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?'], ['How can I increase the speed of my internet connection while using a VPN?', 'How can Internet speed be increased by hacking through DNS?'], ['Why am I mentally very lonely? How can I solve it?', 'Find the remainder when [math]23^{24}[/math] is divided by 24,23?'], ['Which one dissolve in water quikly sugar, salt, methane and carbon di oxide?', 'Which fish would survive in salt water?'], ] scores = model.predict(pairs) print(scores.shape) # [5] # ... or rank different texts based on similarity to a single text ranks = model.rank( 'What is the step by step guide to invest in share market in india?', [ 'What is the step by step guide to invest in share market?', 'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?', 'How can Internet speed be increased by hacking through DNS?', 'Find the remainder when [math]23^{24}[/math] is divided by 24,23?', 'Which fish would survive in salt water?', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Evaluation ### Metrics #### Cross Encoder Classification * Datasets: `quora-duplicates-dev` and `quora-duplicates-test` * Evaluated with [CEClassificationEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEClassificationEvaluator) | Metric | quora-duplicates-dev | quora-duplicates-test | |:----------------------|:---------------------|:----------------------| | accuracy | 0.8938 | 0.8938 | | accuracy_threshold | 0.5089 | 0.5091 | | f1 | 0.8612 | 0.8612 | | f1_threshold | 0.3856 | 0.3858 | | precision | 0.8183 | 0.8183 | | recall | 0.9089 | 0.9089 | | **average_precision** | **0.9203** | **0.9203** | ## Training Details ### Training Dataset #### quora-duplicates * Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 404,290 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------------| | What are the features of the Indian caste system? | What triggers you the most when you play video games? | 0 | | What is the best place to learn Mandarin Chinese in Singapore? | What is the best place in Singapore for durian in December? | 0 | | What will be Hillary Clinton's India policy if she wins the election? | How would the bilateral relationship between India and the USA be under Hillary Clinton's presidency? | 1 | * Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#binarycrossentropyloss) ### Evaluation Dataset #### quora-duplicates * Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 404,290 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------| | What is the step by step guide to invest in share market in india? | What is the step by step guide to invest in share market? | 0 | | What is the story of Kohinoor (Koh-i-Noor) Diamond? | What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back? | 0 | | How can I increase the speed of my internet connection while using a VPN? | How can Internet speed be increased by hacking through DNS? | 0 | * Loss: [BinaryCrossEntropyLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#binarycrossentropyloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: 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`: 5e-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`: 42 - `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`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `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 | Validation Loss | quora-duplicates-dev_average_precision | quora-duplicates-test_average_precision | |:------:|:----:|:-------------:|:---------------:|:--------------------------------------:|:---------------------------------------:| | -1 | -1 | - | - | 0.3711 | - | | 0.0167 | 100 | 0.6574 | - | - | - | | 0.0333 | 200 | 0.4804 | - | - | - | | 0.0500 | 300 | 0.4406 | - | - | - | | 0.0666 | 400 | 0.4208 | - | - | - | | 0.0833 | 500 | 0.3929 | 0.3958 | 0.8210 | - | | 0.0999 | 600 | 0.3986 | - | - | - | | 0.1166 | 700 | 0.3743 | - | - | - | | 0.1332 | 800 | 0.3938 | - | - | - | | 0.1499 | 900 | 0.3602 | - | - | - | | 0.1665 | 1000 | 0.3714 | 0.3437 | 0.8565 | - | | 0.1832 | 1100 | 0.3486 | - | - | - | | 0.1998 | 1200 | 0.3479 | - | - | - | | 0.2165 | 1300 | 0.3417 | - | - | - | | 0.2331 | 1400 | 0.3425 | - | - | - | | 0.2498 | 1500 | 0.3353 | 0.3264 | 0.8742 | - | | 0.2664 | 1600 | 0.3335 | - | - | - | | 0.2831 | 1700 | 0.3274 | - | - | - | | 0.2998 | 1800 | 0.3284 | - | - | - | | 0.3164 | 1900 | 0.3118 | - | - | - | | 0.3331 | 2000 | 0.3073 | 0.3282 | 0.8826 | - | | 0.3497 | 2100 | 0.3233 | - | - | - | | 0.3664 | 2200 | 0.3072 | - | - | - | | 0.3830 | 2300 | 0.314 | - | - | - | | 0.3997 | 2400 | 0.3065 | - | - | - | | 0.4163 | 2500 | 0.3046 | 0.2877 | 0.8930 | - | | 0.4330 | 2600 | 0.2857 | - | - | - | | 0.4496 | 2700 | 0.285 | - | - | - | | 0.4663 | 2800 | 0.2957 | - | - | - | | 0.4829 | 2900 | 0.2965 | - | - | - | | 0.4996 | 3000 | 0.2824 | 0.2842 | 0.8998 | - | | 0.5162 | 3100 | 0.3019 | - | - | - | | 0.5329 | 3200 | 0.2841 | - | - | - | | 0.5495 | 3300 | 0.2981 | - | - | - | | 0.5662 | 3400 | 0.2878 | - | - | - | | 0.5828 | 3500 | 0.278 | 0.2803 | 0.9061 | - | | 0.5995 | 3600 | 0.2841 | - | - | - | | 0.6162 | 3700 | 0.2794 | - | - | - | | 0.6328 | 3800 | 0.2808 | - | - | - | | 0.6495 | 3900 | 0.27 | - | - | - | | 0.6661 | 4000 | 0.2719 | 0.2697 | 0.9091 | - | | 0.6828 | 4100 | 0.2792 | - | - | - | | 0.6994 | 4200 | 0.2669 | - | - | - | | 0.7161 | 4300 | 0.2696 | - | - | - | | 0.7327 | 4400 | 0.2642 | - | - | - | | 0.7494 | 4500 | 0.2684 | 0.2591 | 0.9140 | - | | 0.7660 | 4600 | 0.2593 | - | - | - | | 0.7827 | 4700 | 0.2756 | - | - | - | | 0.7993 | 4800 | 0.2584 | - | - | - | | 0.8160 | 4900 | 0.2525 | - | - | - | | 0.8326 | 5000 | 0.267 | 0.2540 | 0.9168 | - | | 0.8493 | 5100 | 0.2612 | - | - | - | | 0.8659 | 5200 | 0.2607 | - | - | - | | 0.8826 | 5300 | 0.2565 | - | - | - | | 0.8993 | 5400 | 0.2432 | - | - | - | | 0.9159 | 5500 | 0.2568 | 0.2489 | 0.9198 | - | | 0.9326 | 5600 | 0.2572 | - | - | - | | 0.9492 | 5700 | 0.2658 | - | - | - | | 0.9659 | 5800 | 0.2568 | - | - | - | | 0.9825 | 5900 | 0.2539 | - | - | - | | 0.9992 | 6000 | 0.2458 | 0.2503 | 0.9203 | - | | -1 | -1 | - | - | - | 0.9203 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.069 kWh - **Carbon Emitted**: 0.027 kg of CO2 - **Hours Used**: 0.214 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.5.0.dev0 - Transformers: 4.49.0.dev0 - PyTorch: 2.5.0+cu121 - Accelerate: 1.3.0 - Datasets: 2.20.0 - 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", } ```