| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:84524 |
| - loss:AttributeTripletLoss |
| base_model: Alibaba-NLP/gte-base-en-v1.5 |
| widget: |
| - source_sentence: Don Piper |
| sentences: |
| - Tommy Nelson |
| - Kate Walbert |
| - publisher |
| - author |
| - source_sentence: The Luxe |
| sentences: |
| - '1999' |
| - publication_date |
| - title |
| - 'Critical Care, Mercy Hospital Series #1' |
| - source_sentence: Bram Stoker |
| sentences: |
| - author |
| - Michael J. Pangio |
| - '9781598871012' |
| - isbn_13 |
| - source_sentence: '9780385340557' |
| sentences: |
| - BBC Books |
| - '9780399208539' |
| - author |
| - isbn_13 |
| - source_sentence: Midnight |
| sentences: |
| - The Bone Parade |
| - 12/01/2005 |
| - publication_date |
| - title |
| 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: 0.1667376458644867 |
| name: Cosine Accuracy |
| - type: cosine_accuracy |
| value: 0.16471828520298004 |
| name: Cosine Accuracy |
| - task: |
| type: silhouette |
| name: Silhouette |
| dataset: |
| name: Unknown |
| type: unknown |
| metrics: |
| - type: silhouette_cosine |
| value: -0.44084376096725464 |
| name: Silhouette Cosine |
| - type: silhouette_euclidean |
| value: 0.015225890092551708 |
| name: Silhouette Euclidean |
| - type: silhouette_cosine |
| value: -0.48077088594436646 |
| name: Silhouette Cosine |
| - type: silhouette_euclidean |
| value: 0.01669926382601261 |
| 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) <!-- at revision a829fd0e060bb84554da0dfd354d0de0f7712b7f --> |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Output Dimensionality:** 768 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/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-book-test-sbert-bs128_lr0.0002_ep3_cosine_snTrue_spFalse_hn1") |
| # Run inference |
| sentences = [ |
| 'Midnight', |
| 'The Bone Parade', |
| '12/01/2005', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### 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.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Triplet |
|
|
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.1667** | |
| |
| #### Silhouette |
| |
| * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> |
| |
| | Metric | Value | |
| |:----------------------|:------------| |
| | **silhouette_cosine** | **-0.4408** | |
| | silhouette_euclidean | 0.0152 | |
| |
| #### Triplet |
| |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:-----------| |
| | **cosine_accuracy** | **0.1647** | |
| |
| #### Silhouette |
| |
| * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> |
| |
| | Metric | Value | |
| |:----------------------|:------------| |
| | **silhouette_cosine** | **-0.4808** | |
| | silhouette_euclidean | 0.0167 | |
|
|
| <!-- |
| ## 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: 84,524 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, and <code>neg_attr_name</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | pos_attr_name | neg_attr_name | |
| |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| |
| | type | string | string | string | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 6.97 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.09 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.31 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.77 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.8 tokens</li><li>max: 5 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | pos_attr_name | neg_attr_name | |
| |:---------------------------|:---------------------------|:------------------------------------------|:------------------------------|:-----------------------| |
| | <code>09/01/1997</code> | <code>12/01/1977</code> | <code>2010</code> | <code>publication_date</code> | <code>title</code> | |
| | <code>9780060275730</code> | <code>9780829748772</code> | <code>HarperCollins Publishers Ltd</code> | <code>isbn_13</code> | <code>publisher</code> | |
| | <code>9780609809648</code> | <code>9780764551956</code> | <code>HarperCollins Publishers</code> | <code>isbn_13</code> | <code>author</code> | |
| * Loss: <code>veriscrape.training.AttributeTripletLoss</code> with these parameters: |
| ```json |
| { |
| "distance_metric": "TripletDistanceMetric.COSINE", |
| "triplet_margin": 5 |
| } |
| ``` |
| |
| ### Evaluation Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 9,392 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, and <code>neg_attr_name</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | pos_attr_name | neg_attr_name | |
| |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| |
| | type | string | string | string | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 6.85 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.98 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.08 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.75 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.8 tokens</li><li>max: 5 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | pos_attr_name | neg_attr_name | |
| |:-------------------------------|:-----------------------------|:---------------------------|:-----------------------|:------------------------------| |
| | <code>9780764200564</code> | <code>: 9780590458467</code> | <code>1984</code> | <code>isbn_13</code> | <code>publication_date</code> | |
| | <code>Penguin Group USA</code> | <code>Signet</code> | <code>9781600243912</code> | <code>publisher</code> | <code>isbn_13</code> | |
| | <code>Alphabet Juice</code> | <code>Space</code> | <code>9780807871133</code> | <code>title</code> | <code>isbn_13</code> | |
| * Loss: <code>veriscrape.training.AttributeTripletLoss</code> with these parameters: |
| ```json |
| { |
| "distance_metric": "TripletDistanceMetric.COSINE", |
| "triplet_margin": 5 |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `eval_strategy`: epoch |
| - `per_device_train_batch_size`: 128 |
| - `per_device_eval_batch_size`: 128 |
| - `learning_rate`: 0.0002 |
| - `warmup_ratio`: 0.1 |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `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`: 0.0002 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 3 |
| - `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 |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | silhouette_cosine | |
| |:-----:|:----:|:-------------:|:---------------:|:---------------:|:-----------------:| |
| | -1 | -1 | - | - | 0.4284 | 0.1492 | |
| | 1.0 | 661 | 4.0498 | 4.3099 | 0.6033 | -0.2282 | |
| | 2.0 | 1322 | 4.4641 | 5.0 | 0.0400 | 0.0 | |
| | 3.0 | 1983 | 4.9651 | 5.0 | 0.1667 | -0.4408 | |
| | -1 | -1 | - | - | 0.1647 | -0.4808 | |
|
|
|
|
| ### 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
