all-mpnet-base-v2-clinc-subset

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2 on the 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: sentence-transformers/all-mpnet-base-v2 <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • clinc150
• Language: en

<!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bnoland/all-mpnet-base-v2-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, -0.2053, -0.4132],
#         [-0.2053,  1.0000,  0.2182],
#         [-0.4132,  0.2182,  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 at 2bbb9af
• 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>

Evaluation Dataset

clinc150

• Dataset: clinc150 at 2bbb9af
• 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>

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

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

@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

@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.* -->