--- language: - en library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - loss:AdaptiveLayerLoss - loss:MultipleNegativesRankingLoss base_model: distilbert/distilroberta-base metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: Certainly. sentences: - '''Of course.''' - The idea is a good one. - the woman is asleep at home - source_sentence: He walked. sentences: - The man was walking. - The people are running. - The women are making pizza. - source_sentence: Double pig. sentences: - Ah, triple pig! - He had no real answer. - Do you not know? - source_sentence: Very simply. sentences: - Not complicatedly. - People are on a beach. - The man kicks the umpire. - source_sentence: Introduction sentences: - Analytical Perspectives. - A man reads the paper. - No one wanted Singapore. pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 94.69690706493431 energy_consumed: 0.24362341090329948 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: 0.849 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer based on distilbert/distilroberta-base results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.845554152020916 name: Pearson Cosine - type: spearman_cosine value: 0.8486455482928023 name: Spearman Cosine - type: pearson_manhattan value: 0.8475103134032791 name: Pearson Manhattan - type: spearman_manhattan value: 0.8505660318245544 name: Spearman Manhattan - type: pearson_euclidean value: 0.8494883021932786 name: Pearson Euclidean - type: spearman_euclidean value: 0.8526835635349959 name: Spearman Euclidean - type: pearson_dot value: 0.7866563719943611 name: Pearson Dot - type: spearman_dot value: 0.7816258810453734 name: Spearman Dot - type: pearson_max value: 0.8494883021932786 name: Pearson Max - type: spearman_max value: 0.8526835635349959 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8182808182081737 name: Pearson Cosine - type: spearman_cosine value: 0.8148039503538166 name: Spearman Cosine - type: pearson_manhattan value: 0.8132463174874629 name: Pearson Manhattan - type: spearman_manhattan value: 0.8088248622918064 name: Spearman Manhattan - type: pearson_euclidean value: 0.8148200486691981 name: Pearson Euclidean - type: spearman_euclidean value: 0.8105059611031759 name: Spearman Euclidean - type: pearson_dot value: 0.7499699563291125 name: Pearson Dot - type: spearman_dot value: 0.7350068244681712 name: Spearman Dot - type: pearson_max value: 0.8182808182081737 name: Pearson Max - type: spearman_max value: 0.8148039503538166 name: Spearman Max --- # SentenceTransformer based on distilbert/distilroberta-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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 SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/distilroberta-base-nli-adaptive-layer") # Run inference sentences = [ 'Introduction', 'Analytical Perspectives.', 'A man reads the paper.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8456 | | **spearman_cosine** | **0.8486** | | pearson_manhattan | 0.8475 | | spearman_manhattan | 0.8506 | | pearson_euclidean | 0.8495 | | spearman_euclidean | 0.8527 | | pearson_dot | 0.7867 | | spearman_dot | 0.7816 | | pearson_max | 0.8495 | | spearman_max | 0.8527 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8183 | | **spearman_cosine** | **0.8148** | | pearson_manhattan | 0.8132 | | spearman_manhattan | 0.8088 | | pearson_euclidean | 0.8148 | | spearman_euclidean | 0.8105 | | pearson_dot | 0.75 | | spearman_dot | 0.735 | | pearson_max | 0.8183 | | spearman_max | 0.8148 | ## Training Details ### Training Dataset #### sentence-transformers/all-nli * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [e587f0c](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/e587f0c494c20fb9a1853cdfb43d42576d60a7e5) * Size: 557,850 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. | | Children smiling and waving at camera | There are children present | The kids are frowning | | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | The boy skates down the sidewalk. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3 } ``` ### Evaluation Dataset #### sentence-transformers/all-nli * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [e587f0c](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/e587f0c494c20fb9a1853cdfb43d42576d60a7e5) * Size: 6,584 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding 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 kids in jackets walk to school. | | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | A woman drinks her coffee in a small cafe. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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 - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `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`: None - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0.0229 | 100 | 7.0517 | 3.9378 | 0.7889 | - | | 0.0459 | 200 | 4.4877 | 3.8105 | 0.7906 | - | | 0.0688 | 300 | 4.0315 | 3.6401 | 0.7966 | - | | 0.0918 | 400 | 3.822 | 3.3537 | 0.7883 | - | | 0.1147 | 500 | 3.0608 | 2.5975 | 0.7973 | - | | 0.1376 | 600 | 2.6304 | 2.3956 | 0.7943 | - | | 0.1606 | 700 | 2.7723 | 2.0379 | 0.8009 | - | | 0.1835 | 800 | 2.3556 | 1.9645 | 0.7984 | - | | 0.2065 | 900 | 2.4998 | 1.9086 | 0.8017 | - | | 0.2294 | 1000 | 2.1834 | 1.8400 | 0.7973 | - | | 0.2524 | 1100 | 2.2793 | 1.5831 | 0.8102 | - | | 0.2753 | 1200 | 2.1042 | 1.6485 | 0.8004 | - | | 0.2982 | 1300 | 2.1365 | 1.7084 | 0.8013 | - | | 0.3212 | 1400 | 2.0096 | 1.5520 | 0.8064 | - | | 0.3441 | 1500 | 2.0492 | 1.4917 | 0.8084 | - | | 0.3671 | 1600 | 1.8764 | 1.5447 | 0.8018 | - | | 0.3900 | 1700 | 1.8611 | 1.5480 | 0.8046 | - | | 0.4129 | 1800 | 1.972 | 1.5353 | 0.8075 | - | | 0.4359 | 1900 | 1.8062 | 1.4633 | 0.8039 | - | | 0.4588 | 2000 | 1.8565 | 1.4213 | 0.8027 | - | | 0.4818 | 2100 | 1.8852 | 1.3860 | 0.8002 | - | | 0.5047 | 2200 | 1.7939 | 1.5468 | 0.7910 | - | | 0.5276 | 2300 | 1.7398 | 1.6041 | 0.7888 | - | | 0.5506 | 2400 | 1.8535 | 1.5791 | 0.7949 | - | | 0.5735 | 2500 | 1.8486 | 1.4871 | 0.7951 | - | | 0.5965 | 2600 | 1.7379 | 1.5427 | 0.8019 | - | | 0.6194 | 2700 | 1.7325 | 1.4585 | 0.8087 | - | | 0.6423 | 2800 | 1.7664 | 1.5264 | 0.7965 | - | | 0.6653 | 2900 | 1.7517 | 1.6344 | 0.7930 | - | | 0.6882 | 3000 | 1.8329 | 1.4947 | 0.8008 | - | | 0.7112 | 3100 | 1.7206 | 1.4917 | 0.8089 | - | | 0.7341 | 3200 | 1.7138 | 1.4185 | 0.8065 | - | | 0.7571 | 3300 | 1.3705 | 1.2040 | 0.8446 | - | | 0.7800 | 3400 | 1.1289 | 1.1363 | 0.8447 | - | | 0.8029 | 3500 | 1.0174 | 1.1049 | 0.8464 | - | | 0.8259 | 3600 | 1.0188 | 1.0362 | 0.8466 | - | | 0.8488 | 3700 | 0.9841 | 1.1391 | 0.8470 | - | | 0.8718 | 3800 | 0.8466 | 1.0116 | 0.8485 | - | | 0.8947 | 3900 | 0.9268 | 1.1323 | 0.8488 | - | | 0.9176 | 4000 | 0.8686 | 1.0296 | 0.8495 | - | | 0.9406 | 4100 | 0.9255 | 1.1737 | 0.8484 | - | | 0.9635 | 4200 | 0.7991 | 1.0609 | 0.8486 | - | | 0.9865 | 4300 | 0.8431 | 0.9976 | 0.8486 | - | | 1.0 | 4359 | - | - | - | 0.8148 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.244 kWh - **Carbon Emitted**: 0.095 kg of CO2 - **Hours Used**: 0.849 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: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - Tokenizers: 0.19.1 ## 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", } ``` #### AdaptiveLayerLoss ```bibtex @misc{li20242d, title={2D Matryoshka Sentence Embeddings}, author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, year={2024}, eprint={2402.14776}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```