--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10089 - loss:AttributeTripletLoss base_model: Alibaba-NLP/gte-base-en-v1.5 widget: - source_sentence: '9780899663975' sentences: - HarperCollins Publishers Inc - publisher - isbn_13 - '9780375703768' - source_sentence: Dorling Kindersley Publishing, Incorporated sentences: - 09/01/1999 - publisher - publication_date - Razorbill - source_sentence: Soho Pr Inc sentences: - Kodansha International Ltd - Nina Malkin - publisher - author - source_sentence: Kathy Reichs sentences: - author - '9781598877625' - Estelle Rankin - isbn_13 - source_sentence: Kevin D. Mitnick sentences: - author - '9781594133725' - isbn_13 - Helen E. Johnson pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - silhouette_cosine - silhouette_euclidean model-index: - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 1.0 name: Cosine Accuracy - type: cosine_accuracy value: 1.0 name: Cosine Accuracy - task: type: silhouette name: Silhouette dataset: name: Unknown type: unknown metrics: - type: silhouette_cosine value: 0.9493929743766785 name: Silhouette Cosine - type: silhouette_euclidean value: 0.8277457356452942 name: Silhouette Euclidean - type: silhouette_cosine value: 0.9458191990852356 name: Silhouette Cosine - type: silhouette_euclidean value: 0.8220656514167786 name: Silhouette Euclidean --- # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5). 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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, '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}) ) ``` ## 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("albertus-sussex/veriscrape-sbert-book-reference_4_to_verify_6-fold-6") # Run inference sentences = [ 'Kevin D. Mitnick', 'Helen E. Johnson', '9781594133725', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Triplet * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:--------| | **cosine_accuracy** | **1.0** | #### Silhouette * Evaluated with veriscrape.training.SilhouetteEvaluator | Metric | Value | |:----------------------|:-----------| | **silhouette_cosine** | **0.9494** | | silhouette_euclidean | 0.8277 | #### Triplet * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:--------| | **cosine_accuracy** | **1.0** | #### Silhouette * Evaluated with veriscrape.training.SilhouetteEvaluator | Metric | Value | |:----------------------|:-----------| | **silhouette_cosine** | **0.9458** | | silhouette_euclidean | 0.8221 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,089 training samples * Columns: anchor, positive, negative, pos_attr_name, and neg_attr_name * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | type | string | string | string | string | string | | details | | | | | | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | |:----------------------------|:------------------------------------|:----------------------------|:------------------------------|:------------------------------| | 01 March 2011 | 02/09/2010 | BBC Active | publication_date | publisher | | Watership Down | The Liberal Way of War | 22/06/2010 | title | publication_date | | Colleen Coble | Neil Gaiman | Dolores Huerta | author | title | * Loss: veriscrape.training.AttributeTripletLoss with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 1,121 evaluation samples * Columns: anchor, positive, negative, pos_attr_name, and neg_attr_name * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | pos_attr_name | neg_attr_name | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | type | string | string | string | string | string | | details | | | | | | * Samples: | anchor | positive | negative | pos_attr_name | neg_attr_name | |:--------------------------------------------------------------------|:-----------------------------|:----------------------------|:-----------------------|:-----------------------| | Broadway Books | Harpercollins | 9780091884635 | publisher | isbn_13 | | Mary Pope Osborne | etc. | 9780571045785 | author | isbn_13 | | 101 Cheap Eats: Tried-and-tested Recipes - "Good Food" | Hunters Of Dune | Hachette Audio | title | publisher | * Loss: veriscrape.training.AttributeTripletLoss with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `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 - `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`: 5 - `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`: 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} - `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`: 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_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | silhouette_cosine | |:-----:|:----:|:-------------:|:---------------:|:---------------:|:-----------------:| | -1 | -1 | - | - | 0.5629 | 0.2074 | | 1.0 | 79 | 0.8615 | 0.0464 | 0.9955 | 0.9038 | | 2.0 | 158 | 0.0222 | 0.0027 | 1.0 | 0.9368 | | 3.0 | 237 | 0.0088 | 0.0048 | 0.9991 | 0.9450 | | 4.0 | 316 | 0.0045 | 0.0027 | 1.0 | 0.9436 | | 5.0 | 395 | 0.0035 | 0.0001 | 1.0 | 0.9494 | | -1 | -1 | - | - | 1.0 | 0.9458 | ### Framework Versions - Python: 3.10.16 - Sentence Transformers: 3.4.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.5.2 - 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", } ``` #### AttributeTripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```