--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:15002 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-large-en-v1.5 widget: - source_sentence: what kind of oil and how much do i need for my toyota tacoma truck and how do i do it sentences: - Requests to change the system or application's language settings. Users may ask to switch to a specific language, such as English, or adjust the language preferences to enhance usability. - Requests for step-by-step instructions or guidance on how to change the oil in a car. Users seek detailed procedures, tools needed, and tips for performing this maintenance task. - Requests to make a reservation at a specific restaurant for a specified number of people, time, and under a provided name. Users expect confirmation of the booking details. - source_sentence: please double check my reservations for six at mani sentences: - Requests to verify or confirm existing reservations, typically for dining or events. Users provide details about the reservation and ask for confirmation that it is correctly recorded. - Requests for details about an insurance policy, including coverage, benefits, and exclusions. Users may inquire about specific aspects like health benefits or policy terms. - Requests to create, manage, or customize timers for various tasks or activities. Users can define the duration, purpose, or type of the timer and receive notifications or alerts when the timer reaches its set time. - source_sentence: what are some good ethiopian restaurants in queens sentences: - Requests for the meaning or definition of words. Users may inquire about the definitions of uncommon, complex, or unfamiliar terms, aiming to gain a clear understanding or contextual usage of the word in question. - Requests to assist with paying bills, such as utilities, credit cards, or other services. Users may specify the bill type, amount, and source account for the payment. - Requests for recommendations or suggestions for dining options. Users may ask for specific cuisine types, locations, or general ideas on where to eat. - source_sentence: are there any expected delays for flight dl123 sentences: - Requests for travel time or distance to a specific location. Users typically seek estimates based on current traffic, routes, or modes of transportation to determine the time needed to reach their destination. - Requests for information about flight details, such as boarding times, delays, or schedules. Users typically inquire to ensure they are updated about their flight's status. - Requests for advice or strategies to improve credit scores. Users may seek a detailed plan, tips, or insights into financial habits that can lead to a better credit rating. - source_sentence: how do i ask about the weather in chinese sentences: - Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation. - Requests for information about a vehicle's miles per gallon (MPG) rating, either in specific conditions like city driving or as an overall performance metric. Users may seek guidance on fuel efficiency for their car. - Requests for information about a vehicle's miles per gallon (MPG) rating, either in specific conditions like city driving or as an overall performance metric. Users may seek guidance on fuel efficiency for their car. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on BAAI/bge-large-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.9706666666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9886666666666667 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.992 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9956666666666667 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9706666666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3295555555555556 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19840000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09956666666666668 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9706666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9886666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.992 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9956666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9841961906084298 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9804173280423282 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9806052445247627 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-large-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### 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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## 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("chinchilla04/bge-finetuned-train") # Run inference sentences = [ 'how do i ask about the weather in chinese', 'Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.', "Requests for information about a vehicle's miles per gallon (MPG) rating, either in specific conditions like city driving or as an overall performance metric. Users may seek guidance on fuel efficiency for their car.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9707 | | cosine_accuracy@3 | 0.9887 | | cosine_accuracy@5 | 0.992 | | cosine_accuracy@10 | 0.9957 | | cosine_precision@1 | 0.9707 | | cosine_precision@3 | 0.3296 | | cosine_precision@5 | 0.1984 | | cosine_precision@10 | 0.0996 | | cosine_recall@1 | 0.9707 | | cosine_recall@3 | 0.9887 | | cosine_recall@5 | 0.992 | | cosine_recall@10 | 0.9957 | | **cosine_ndcg@10** | **0.9842** | | cosine_mrr@10 | 0.9804 | | cosine_map@100 | 0.9806 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 15,002 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 | |:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what expression would i use to say i love you if i were an italian | Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation. | Requests involving financial operations, such as transferring money between bank accounts, credit cards, or other financial instruments. Users typically specify the amount, the source account, and the target account, ensuring that the transfer is executed correctly and securely. | | can you tell me how to say 'i do not speak much spanish', in spanish | Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation. | Requests involving financial operations, such as transferring money between bank accounts, credit cards, or other financial instruments. Users typically specify the amount, the source account, and the target account, ensuring that the transfer is executed correctly and securely. | | what is the equivalent of, 'life is good' in french | Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation. | Requests involving financial operations, such as transferring money between bank accounts, credit cards, or other financial instruments. Users typically specify the amount, the source account, and the target account, ensuring that the transfer is executed correctly and securely. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 3,000 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | in spanish, meet me tomorrow is said how | Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation. | | in french, how do i say, see you later | Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation. | | how do you say hello in japanese | Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `learning_rate`: 1e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.2 - `load_best_model_at_end`: True - `optim`: adamw_torch_fused #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 8 - `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`: 1e-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`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.2 - `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`: 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`: 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_fused - `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`: False - `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 | cosine_ndcg@10 | |:----------:|:--------:|:-------------:|:---------------:|:--------------:| | None | 0 | - | 0.2730 | 0.9055 | | 0.3198 | 150 | - | 0.0698 | 0.9633 | | 0.6397 | 300 | - | 0.0642 | 0.9683 | | 0.9595 | 450 | - | 0.0603 | 0.9763 | | 1.0661 | 500 | 1.0338 | - | - | | 1.2793 | 600 | - | 0.0612 | 0.9762 | | 1.5991 | 750 | - | 0.0602 | 0.9802 | | 1.9190 | 900 | - | 0.0571 | 0.9820 | | 2.1322 | 1000 | 0.787 | - | - | | 2.2388 | 1050 | - | 0.0585 | 0.9819 | | **2.5586** | **1200** | **-** | **0.0565** | **0.9842** | | 2.8785 | 1350 | - | 0.0578 | 0.9837 | | 3.1983 | 1500 | 0.6768 | 0.0570 | 0.9844 | | 3.5181 | 1650 | - | 0.0587 | 0.9837 | | 3.8380 | 1800 | - | 0.0584 | 0.9837 | | None | 0 | - | 0.0565 | 0.9842 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.3.1 - Transformers: 4.46.3 - PyTorch: 2.4.0 - Accelerate: 1.1.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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", } ``` #### 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} } ```