| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:42272 |
| - loss:MultipleNegativesRankingLoss |
| base_model: BAAI/bge-large-en-v1.5 |
| widget: |
| - source_sentence: Pegasus standing right |
| sentences: |
| - Concordia standing with cornucopia and branch, head facing right. |
| - Pegasus walking right |
| - Victory advancing left, holding wreath and palm-branch. |
| - source_sentence: Felicitas seated left, holding caduceus in right hand and cornucopia |
| cradled in left arm, SMT in exergue |
| sentences: |
| - Providentia draped standing facing, looking left, holding a globe in the right |
| hand and a transverse sceptre in the left. |
| - Victory walking left, holding a palm and a crown. |
| - Genius standing left, holding patera and cornucopia; two stars in left field; |
| crescent over Z in right; ANT in exergue. |
| - source_sentence: Armored bust of Mars with helmet to the right, seen from the front. |
| sentences: |
| - Emperor in field dress with Victoria on globe and labarum standing to the right, |
| left foot on a lying, bound prisoner. |
| - Roma, helmeted and draped, standing left, holding a globe topped with a phoenix |
| in the right hand and a transverse sceptre in the left; behind, a shield. |
| - Eagle standing facing with wings spread, head left |
| - source_sentence: Prow of galley right |
| sentences: |
| - Salus seated left, feeding from patera a serpent rising from altar. |
| - The Dea Caelestis riding right on a lion, holding a drum in right hand and scepter |
| in left; below, water gushing from rock with inscription IN CARTH. |
| - Galley sailing to the left with rowers. |
| - source_sentence: Providentia standing left, holding globe and cornucopiae |
| sentences: |
| - Fides Milites seated left |
| - Jupiter to the left and Hercules to the right, standing face to face shaking hands; |
| Jupiter holds a long spear in his left hand with cloak flowing over his right |
| shoulder; Hercules holds his club in his left hand around which the lion skin |
| is wrapped. |
| - Sol in quadriga left, holding globe and whip, raising right hand, R thunderbolt |
| Γ in ex. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # 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) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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, 'architecture': '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("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'Providentia standing left, holding globe and cornucopiae', |
| 'Fides Milites seated left', |
| 'Jupiter to the left and Hercules to the right, standing face to face shaking hands; Jupiter holds a long spear in his left hand with cloak flowing over his right shoulder; Hercules holds his club in his left hand around which the lion skin is wrapped.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 1024] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.3315, 0.3332], |
| # [0.3315, 1.0000, 0.3473], |
| # [0.3332, 0.3473, 1.0000]]) |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 42,272 training samples |
| * Columns: <code>sentence_0</code> and <code>sentence_1</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence_0 | sentence_1 | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 20.12 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 19.79 tokens</li><li>max: 75 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | |
| |:-------------------------------------------------------------------------------|:---------------------------------------------------------------------| |
| | <code>Felicitas standing to the left holding a caduceus and cornucopia.</code> | <code>Felicitas standing with caduceus and cornucopia.</code> | |
| | <code>S P Q R/OB/C S in three lines within oak wreath</code> | <code>Legend in three lines within oak wreath</code> | |
| | <code>Iustitia seated to the left holding patera and scepter</code> | <code>Iustitia seated to the left holding patera and scepter.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `num_train_epochs`: 1 |
| - `max_steps`: 2642 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `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 |
| - `num_train_epochs`: 1 |
| - `max_steps`: 2642 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: 0.0 |
| - `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 |
| - `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`: 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} |
| - `parallelism_config`: 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 |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `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 |
| - `hub_revision`: None |
| - `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 |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 0.0189 | 50 | - | |
| | 0.0379 | 100 | - | |
| | 0.0568 | 150 | - | |
| | 0.0757 | 200 | - | |
| | 0.0946 | 250 | - | |
| | 0.1136 | 300 | - | |
| | 0.1325 | 350 | - | |
| | 0.1514 | 400 | - | |
| | 0.1703 | 450 | - | |
| | 0.1893 | 500 | 1.0463 | |
| | 0.2082 | 550 | - | |
| | 0.2271 | 600 | - | |
| | 0.2460 | 650 | - | |
| | 0.2650 | 700 | - | |
| | 0.2839 | 750 | - | |
| | 0.3028 | 800 | - | |
| | 0.3217 | 850 | - | |
| | 0.3407 | 900 | - | |
| | 0.3596 | 950 | - | |
| | 0.3785 | 1000 | 0.9948 | |
| | 0.3974 | 1050 | - | |
| | 0.4164 | 1100 | - | |
| | 0.4353 | 1150 | - | |
| | 0.4542 | 1200 | - | |
| | 0.4731 | 1250 | - | |
| | 0.4921 | 1300 | - | |
| | 0.5110 | 1350 | - | |
| | 0.5299 | 1400 | - | |
| | 0.5488 | 1450 | - | |
| | 0.5678 | 1500 | 0.9288 | |
| | 0.5867 | 1550 | - | |
| | 0.6056 | 1600 | - | |
| | 0.6245 | 1650 | - | |
| | 0.6435 | 1700 | - | |
| | 0.6624 | 1750 | - | |
| | 0.6813 | 1800 | - | |
| | 0.7002 | 1850 | - | |
| | 0.7192 | 1900 | - | |
| | 0.7381 | 1950 | - | |
| | 0.7570 | 2000 | 0.9219 | |
| | 0.7759 | 2050 | - | |
| | 0.7949 | 2100 | - | |
| | 0.8138 | 2150 | - | |
| | 0.8327 | 2200 | - | |
| | 0.8516 | 2250 | - | |
| | 0.8706 | 2300 | - | |
| | 0.8895 | 2350 | - | |
| | 0.9084 | 2400 | - | |
| | 0.9273 | 2450 | - | |
| | 0.9463 | 2500 | 0.8954 | |
| | 0.9652 | 2550 | - | |
| | 0.9841 | 2600 | - | |
| | 1.0 | 2642 | - | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.2.3 |
| - Transformers: 4.57.6 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.3.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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} |
| } |
| ``` |
|
|
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| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| ## Model Card Contact |
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