| | --- |
| | language: |
| | - en |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - dense |
| | - generated_from_trainer |
| | - dataset_size:3000 |
| | - loss:BatchAllTripletLoss |
| | base_model: microsoft/mpnet-base |
| | widget: |
| | - source_sentence: what am i supposed to do if i lost my luggage |
| | sentences: |
| | - do i need a visa if i go there |
| | - why did you freeze my bank account |
| | - tell my bank that i'm travelling to france in 2 days |
| | - source_sentence: can you suggest some of the most popular travel destination |
| | sentences: |
| | - what is the total of my repair bill |
| | - could you tell me my bill's minimum payment |
| | - can you get me a car rental for march 1st to 3rd in seattle, and i'd like a sedan |
| | if possible |
| | - source_sentence: is there a minimum amount accepted |
| | sentences: |
| | - am i going to need a visa for traveling to canada |
| | - submit payment to duke energy for my electric bill |
| | - let me know chase's routing number |
| | - source_sentence: my account appears to be blocked and i don't know why |
| | sentences: |
| | - how do you say hello in japanese |
| | - how much is due on the gas bill |
| | - how much was my last transaction for |
| | - source_sentence: are there any travel alerts for juarez |
| | sentences: |
| | - i am now out of checks, how do i order new ones |
| | - lowest amount for cable bill |
| | - how much interest do i get on my citizen's savings account |
| | datasets: |
| | - contemmcm/clinc150 |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | --- |
| | |
| | # SentenceTransformer based on microsoft/mpnet-base |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [clinc150](https://huggingface.co/datasets/contemmcm/clinc150) dataset. 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [clinc150](https://huggingface.co/datasets/contemmcm/clinc150) |
| | - **Language:** en |
| | <!-- - **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': False, 'architecture': 'MPNetModel'}) |
| | (1): Pooling({'word_embedding_dimension': 768, '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("bnoland/mpnet-base-clinc-subset") |
| | # Run inference |
| | sentences = [ |
| | 'are there any travel alerts for juarez', |
| | "how much interest do i get on my citizen's savings account", |
| | 'lowest amount for cable bill', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 768] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities) |
| | # tensor([[1.0000, 0.7056, 0.6717], |
| | # [0.7056, 1.0000, 0.7377], |
| | # [0.6717, 0.7377, 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 |
| |
|
| | #### clinc150 |
| |
|
| | * Dataset: [clinc150](https://huggingface.co/datasets/contemmcm/clinc150) at [2bbb9af](https://huggingface.co/datasets/contemmcm/clinc150/tree/2bbb9afebdafb9b9f6719250310bfcf3b1e8f666) |
| | * Size: 3,000 training samples |
| | * Columns: <code>text</code> and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | text | label | |
| | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | type | string | int | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 12.61 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>1: ~3.60%</li><li>2: ~3.80%</li><li>3: ~3.50%</li><li>4: ~4.30%</li><li>5: ~3.90%</li><li>6: ~3.50%</li><li>7: ~2.20%</li><li>8: ~3.00%</li><li>9: ~2.80%</li><li>10: ~2.90%</li><li>11: ~3.70%</li><li>12: ~2.80%</li><li>13: ~3.70%</li><li>14: ~2.80%</li><li>15: ~3.90%</li><li>76: ~3.60%</li><li>77: ~3.40%</li><li>78: ~3.60%</li><li>79: ~3.40%</li><li>80: ~3.20%</li><li>81: ~3.70%</li><li>82: ~3.00%</li><li>83: ~2.90%</li><li>84: ~3.30%</li><li>85: ~3.50%</li><li>86: ~3.70%</li><li>87: ~2.40%</li><li>88: ~3.70%</li><li>89: ~2.70%</li><li>90: ~3.50%</li></ul> | |
| | * Samples: |
| | | text | label | |
| | |:---------------------------------------------------------------------|:----------------| |
| | | <code>is there enough money in my bank of hawaii for vacation</code> | <code>12</code> | |
| | | <code>i need to let my bank know i am visiting asia soon</code> | <code>77</code> | |
| | | <code>what's bank of america's routing number</code> | <code>2</code> | |
| | * Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss) |
| |
|
| | ### Evaluation Dataset |
| |
|
| | #### clinc150 |
| |
|
| | * Dataset: [clinc150](https://huggingface.co/datasets/contemmcm/clinc150) at [2bbb9af](https://huggingface.co/datasets/contemmcm/clinc150/tree/2bbb9afebdafb9b9f6719250310bfcf3b1e8f666) |
| | * Size: 600 evaluation samples |
| | * Columns: <code>text</code> and <code>label</code> |
| | * Approximate statistics based on the first 600 samples: |
| | | | text | label | |
| | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | type | string | int | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 12.83 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>1: ~3.33%</li><li>2: ~3.33%</li><li>3: ~3.33%</li><li>4: ~3.33%</li><li>5: ~3.33%</li><li>6: ~3.33%</li><li>7: ~3.33%</li><li>8: ~3.33%</li><li>9: ~3.33%</li><li>10: ~3.33%</li><li>11: ~3.33%</li><li>12: ~3.33%</li><li>13: ~3.33%</li><li>14: ~3.33%</li><li>15: ~3.33%</li><li>76: ~3.33%</li><li>77: ~3.33%</li><li>78: ~3.33%</li><li>79: ~3.33%</li><li>80: ~3.33%</li><li>81: ~3.33%</li><li>82: ~3.33%</li><li>83: ~3.33%</li><li>84: ~3.33%</li><li>85: ~3.33%</li><li>86: ~3.33%</li><li>87: ~3.33%</li><li>88: ~3.33%</li><li>89: ~3.33%</li><li>90: ~3.33%</li></ul> | |
| | * Samples: |
| | | text | label | |
| | |:------------------------------------------------------------------|:----------------| |
| | | <code>was my last transaction at walmart</code> | <code>14</code> | |
| | | <code>what interest rate is us bank giving me on my acount</code> | <code>7</code> | |
| | | <code>look up carry-on rules for american airlines</code> | <code>89</code> | |
| | * Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss) |
| |
|
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `num_train_epochs`: 1 |
| | - `warmup_steps`: 10 |
| | - `fp16`: True |
| | - `batch_sampler`: group_by_label |
| |
|
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| |
|
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `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`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: None |
| | - `warmup_ratio`: None |
| | - `warmup_steps`: 10 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `enable_jit_checkpoint`: False |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `use_cpu`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `bf16`: False |
| | - `fp16`: True |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: None |
| | - `local_rank`: -1 |
| | - `ddp_backend`: None |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: False |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `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 |
| | - `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 |
| | - `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_for_metrics`: [] |
| | - `eval_do_concat_batches`: True |
| | - `auto_find_batch_size`: False |
| | - `full_determinism`: False |
| | - `ddp_timeout`: 1800 |
| | - `torch_compile`: False |
| | - `torch_compile_backend`: None |
| | - `torch_compile_mode`: None |
| | - `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 |
| | - `use_cache`: False |
| | - `prompts`: None |
| | - `batch_sampler`: group_by_label |
| | - `multi_dataset_batch_sampler`: proportional |
| | - `router_mapping`: {} |
| | - `learning_rate_mapping`: {} |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Training Loss | Validation Loss | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.5319 | 100 | 0.5093 | 1.7369 | |
| |
|
| |
|
| | ### Framework Versions |
| | - Python: 3.12.12 |
| | - Sentence Transformers: 5.2.3 |
| | - Transformers: 5.0.0 |
| | - PyTorch: 2.10.0+cu128 |
| | - Accelerate: 1.12.0 |
| | - Datasets: 4.0.0 |
| | - 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", |
| | } |
| | ``` |
| |
|
| | #### BatchAllTripletLoss |
| | ```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.* |
| | --> |
