--- language: - en library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - loss:MSELoss base_model: nreimers/TinyBERT_L-4_H-312_v2 metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - negative_mse widget: - source_sentence: A woman at home. sentences: - The woman is inside. - The woman is performing for an audience. - The two men are freinds - source_sentence: boys play football sentences: - Rival college football players are playing a football game. - A man looks at his watch at a bus stop. - A woman walking on an old bridge near a mountain. - source_sentence: Nobody has a pot sentences: - Nobody has a suit - A woman riding a bicycle on the street. - The front is decorated with Ethiopian themes and motifs. - source_sentence: A dog plays ball. sentences: - A dog with a ball. - A man looking into a microscope in a lab - Children go past their parents. - source_sentence: A person standing sentences: - There is a person standing outside - A young man plays a racing video game. - Two children playing on the floor with toy trains. pipeline_tag: sentence-similarity co2_eq_emissions: emissions: 3.457859864142588 energy_consumed: 0.00889591477312334 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: 0.054 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SentenceTransformer based on nreimers/TinyBERT_L-4_H-312_v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8077673131159315 name: Pearson Cosine - type: spearman_cosine value: 0.8208863013753134 name: Spearman Cosine - type: pearson_manhattan value: 0.8225516575982812 name: Pearson Manhattan - type: spearman_manhattan value: 0.8203236078973807 name: Spearman Manhattan - type: pearson_euclidean value: 0.8215663439432439 name: Pearson Euclidean - type: spearman_euclidean value: 0.8202318953605339 name: Spearman Euclidean - type: pearson_dot value: 0.7901487535994149 name: Pearson Dot - type: spearman_dot value: 0.7914362691291718 name: Spearman Dot - type: pearson_max value: 0.8225516575982812 name: Pearson Max - type: spearman_max value: 0.8208863013753134 name: Spearman Max - task: type: knowledge-distillation name: Knowledge Distillation dataset: name: Unknown type: unknown metrics: - type: negative_mse value: -50.125449895858765 name: Negative Mse - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.7516961775809978 name: Pearson Cosine - type: spearman_cosine value: 0.7558402072520215 name: Spearman Cosine - type: pearson_manhattan value: 0.7762734499549059 name: Pearson Manhattan - type: spearman_manhattan value: 0.75965556867712 name: Spearman Manhattan - type: pearson_euclidean value: 0.7705568379382428 name: Pearson Euclidean - type: spearman_euclidean value: 0.7553604477247078 name: Spearman Euclidean - type: pearson_dot value: 0.7306801501272192 name: Pearson Dot - type: spearman_dot value: 0.7097993872384684 name: Spearman Dot - type: pearson_max value: 0.7762734499549059 name: Pearson Max - type: spearman_max value: 0.75965556867712 name: Spearman Max --- # SentenceTransformer based on nreimers/TinyBERT_L-4_H-312_v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/TinyBERT_L-4_H-312_v2](https://huggingface.co/nreimers/TinyBERT_L-4_H-312_v2) on the [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) dataset. It maps sentences & paragraphs to a 312-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:** [nreimers/TinyBERT_L-4_H-312_v2](https://huggingface.co/nreimers/TinyBERT_L-4_H-312_v2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 312 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) - **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: BertModel (1): Pooling({'word_embedding_dimension': 312, '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/TinyBERT_L-4_H-312_v2-distilled-from-stsb-roberta-base-v2") # Run inference sentences = [ 'A person standing', 'There is a person standing outside', 'A young man plays a racing video game.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 312] # 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 | 0.8078 | | **spearman_cosine** | **0.8209** | | pearson_manhattan | 0.8226 | | spearman_manhattan | 0.8203 | | pearson_euclidean | 0.8216 | | spearman_euclidean | 0.8202 | | pearson_dot | 0.7901 | | spearman_dot | 0.7914 | | pearson_max | 0.8226 | | spearman_max | 0.8209 | #### Knowledge Distillation * Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) | Metric | Value | |:-----------------|:-------------| | **negative_mse** | **-50.1254** | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7517 | | **spearman_cosine** | **0.7558** | | pearson_manhattan | 0.7763 | | spearman_manhattan | 0.7597 | | pearson_euclidean | 0.7706 | | spearman_euclidean | 0.7554 | | pearson_dot | 0.7307 | | spearman_dot | 0.7098 | | pearson_max | 0.7763 | | spearman_max | 0.7597 | ## Training Details ### Training Dataset #### sentence-transformers/wikipedia-en-sentences * Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422) * Size: 200,000 training samples * Columns: sentence and label * Approximate statistics based on the first 1000 samples: | | sentence | label | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | sentence | label | |:---------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------| | A person on a horse jumps over a broken down airplane. | [-0.09614687412977219, 0.6815224885940552, 2.702199935913086, 1.8371250629425049, -1.2949433326721191, ...] | | Children smiling and waving at camera | [2.769360303878784, 3.074428081512451, -7.291755676269531, 5.248741149902344, 2.85081148147583, ...] | | A boy is jumping on skateboard in the middle of a red bridge. | [-3.0669667720794678, 2.9899890422821045, -1.253997802734375, 6.15218448638916, 0.5838223099708557, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) ### Evaluation Dataset #### sentence-transformers/wikipedia-en-sentences * Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422) * Size: 10,000 evaluation samples * Columns: sentence and label * Approximate statistics based on the first 1000 samples: | | sentence | label | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------| | type | string | list | | details | | | * Samples: | sentence | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------| | Two women are embracing while holding to go packages. | [6.200135707855225, -2.0865142345428467, -2.1313390731811523, -1.9593913555145264, -1.081985592842102, ...] | | 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. | [1.7725015878677368, 0.6873414516448975, -2.5191268920898438, 3.866339683532715, 2.853647470474243, ...] | | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | [-3.317653179168701, 3.0908589363098145, 0.1683920919895172, -2.4405274391174316, -3.1366524696350098, ...] | * Loss: [MSELoss](https://sbert.net/docs/package_reference/losses.html#mseloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 0.0001 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `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 - `learning_rate`: 0.0001 - `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`: 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`: 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`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | negative_mse | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:--------:|:--------:|:-------------:|:----------:|:------------:|:-----------------------:|:------------------------:| | 0.032 | 100 | 0.8847 | - | - | - | - | | 0.064 | 200 | 0.8136 | - | - | - | - | | 0.096 | 300 | 0.697 | - | - | - | - | | 0.128 | 400 | 0.6128 | - | - | - | - | | 0.16 | 500 | 0.5634 | 0.6324 | -63.2356 | 0.7564 | - | | 0.192 | 600 | 0.5294 | - | - | - | - | | 0.224 | 700 | 0.5035 | - | - | - | - | | 0.256 | 800 | 0.4861 | - | - | - | - | | 0.288 | 900 | 0.4668 | - | - | - | - | | 0.32 | 1000 | 0.4515 | 0.5673 | -56.7263 | 0.7965 | - | | 0.352 | 1100 | 0.4376 | - | - | - | - | | 0.384 | 1200 | 0.4274 | - | - | - | - | | 0.416 | 1300 | 0.4178 | - | - | - | - | | 0.448 | 1400 | 0.4098 | - | - | - | - | | 0.48 | 1500 | 0.4053 | 0.5354 | -53.5381 | 0.8091 | - | | 0.512 | 1600 | 0.3934 | - | - | - | - | | 0.544 | 1700 | 0.391 | - | - | - | - | | 0.576 | 1800 | 0.3848 | - | - | - | - | | 0.608 | 1900 | 0.3785 | - | - | - | - | | 0.64 | 2000 | 0.3737 | 0.5168 | -51.6829 | 0.8159 | - | | 0.672 | 2100 | 0.3716 | - | - | - | - | | 0.704 | 2200 | 0.3695 | - | - | - | - | | 0.736 | 2300 | 0.3666 | - | - | - | - | | 0.768 | 2400 | 0.3616 | - | - | - | - | | 0.8 | 2500 | 0.358 | 0.5067 | -50.6687 | 0.8189 | - | | 0.832 | 2600 | 0.3551 | - | - | - | - | | 0.864 | 2700 | 0.3544 | - | - | - | - | | 0.896 | 2800 | 0.3524 | - | - | - | - | | 0.928 | 2900 | 0.3524 | - | - | - | - | | **0.96** | **3000** | **0.3529** | **0.5013** | **-50.1254** | **0.8209** | **-** | | 0.992 | 3100 | 0.3496 | - | - | - | - | | 1.0 | 3125 | - | - | - | - | 0.7558 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.009 kWh - **Carbon Emitted**: 0.003 kg of CO2 - **Hours Used**: 0.054 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", } ``` #### MSELoss ```bibtex @inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", } ```