--- language: - en tags: - sentence-transformers - cross-encoder - text-classification - generated_from_trainer - dataset_size:942069 - loss:CrossEntropyLoss base_model: distilbert/distilroberta-base datasets: - sentence-transformers/all-nli pipeline_tag: text-classification library_name: sentence-transformers metrics: - f1_macro - f1_micro - f1_weighted co2_eq_emissions: emissions: 5.804161792857238 energy_consumed: 0.01493216343846247 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: 0.058 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: AllNLI dev type: AllNLI-dev metrics: - type: f1_macro value: 0.8495346395196971 name: F1 Macro - type: f1_micro value: 0.851 name: F1 Micro - type: f1_weighted value: 0.8494545162410544 name: F1 Weighted - task: type: cross-encoder-classification name: Cross Encoder Classification dataset: name: AllNLI test type: AllNLI-test metrics: - type: f1_macro value: 0.7574494684363943 name: F1 Macro - type: f1_micro value: 0.7575803825803826 name: F1 Micro - type: f1_weighted value: 0.7582587136974347 name: F1 Weighted --- # 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 [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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:** - [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - **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("tomaarsen/reranker-distilroberta-base-nli") # Get scores for pairs... pairs = [ ['Two women are embracing while holding to go packages.', 'The sisters are hugging goodbye while holding to go packages after just eating lunch.'], ['Two women are embracing while holding to go packages.', 'Two woman are holding packages.'], ['Two women are embracing while holding to go packages.', 'The men are fighting outside a deli.'], ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids in numbered jerseys wash their hands.'], ['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids at a ballgame wash their hands.'], ] scores = model.predict(pairs) print(scores.shape) # [5] # ... or rank different texts based on similarity to a single text ranks = model.rank( 'Two women are embracing while holding to go packages.', [ 'The sisters are hugging goodbye while holding to go packages after just eating lunch.', 'Two woman are holding packages.', 'The men are fighting outside a deli.', 'Two kids in numbered jerseys wash their hands.', 'Two kids at a ballgame wash their hands.', ] ) # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] ``` ## Evaluation ### Metrics #### Cross Encoder Classification * Datasets: `AllNLI-dev` and `AllNLI-test` * Evaluated with [CEClassificationEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEClassificationEvaluator) | Metric | AllNLI-dev | AllNLI-test | |:-------------|:-----------|:------------| | **f1_macro** | **0.8495** | **0.7574** | | f1_micro | 0.851 | 0.7576 | | f1_weighted | 0.8495 | 0.7583 | ## Training Details ### Training Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 942,069 training samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------| | A person on a horse jumps over a broken down airplane. | A person is training his horse for a competition. | 1 | | A person on a horse jumps over a broken down airplane. | A person is at a diner, ordering an omelette. | 2 | | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | 0 | * Loss: [CrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss) ### Evaluation Dataset #### all-nli * Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 19,657 evaluation samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------| | Two women are embracing while holding to go packages. | The sisters are hugging goodbye while holding to go packages after just eating lunch. | 1 | | Two women are embracing while holding to go packages. | Two woman are holding packages. | 0 | | Two women are embracing while holding to go packages. | The men are fighting outside a deli. | 2 | * Loss: [CrossEntropyLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss) ### 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 | AllNLI-dev_f1_macro | AllNLI-test_f1_macro | |:------:|:----:|:-------------:|:---------------:|:-------------------:|:--------------------:| | -1 | -1 | - | - | 0.1677 | - | | 0.0640 | 100 | 1.0454 | - | - | - | | 0.1280 | 200 | 0.7193 | - | - | - | | 0.1919 | 300 | 0.6247 | - | - | - | | 0.2559 | 400 | 0.5907 | - | - | - | | 0.3199 | 500 | 0.5671 | 0.4578 | 0.8206 | - | | 0.3839 | 600 | 0.5384 | - | - | - | | 0.4479 | 700 | 0.5492 | - | - | - | | 0.5118 | 800 | 0.5281 | - | - | - | | 0.5758 | 900 | 0.5043 | - | - | - | | 0.6398 | 1000 | 0.5243 | 0.4012 | 0.8415 | - | | 0.7038 | 1100 | 0.4906 | - | - | - | | 0.7678 | 1200 | 0.4877 | - | - | - | | 0.8317 | 1300 | 0.4506 | - | - | - | | 0.8957 | 1400 | 0.4728 | - | - | - | | 0.9597 | 1500 | 0.4602 | 0.3731 | 0.8495 | - | | -1 | -1 | - | - | - | 0.7574 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.015 kWh - **Carbon Emitted**: 0.006 kg of CO2 - **Hours Used**: 0.058 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", } ```