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bnoland/all-mpnet-base-v2-clinc-subset

sourceHugging Faceupdated 7mo agoView on Hugging Face
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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

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:

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/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]])

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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: groupbylabel
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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
  • —parallelism_config: None
  • —deepspeed: None
  • —label_smoothing_factor: 0.0
  • —optim: adamwtorchfused
  • —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: groupbylabel
  • —multi_dataset_batch_sampler: proportional
  • —router_mapping: {}
  • —learning_rate_mapping: {}

</details>

Training Logs

EpochStepTraining LossValidation Loss
0.53191000.52912.4324

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}
}

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