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Marco127/Argu_T2

sourceHugging Faceupdated 2y agoView on Hugging Face
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SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-dot-v1

This is a sentence-transformers model finetuned from sentence-transformers/multi-qa-mpnet-base-dot-v1. 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/multi-qa-mpnet-base-dot-v1 <!-- at revision 4633e80e17ea975bc090c97b049da26062b054d3 -->
  • —Maximum Sequence Length: 512 tokens
  • —Output Dimensionality: 768 dimensions
  • —Similarity Function: Dot Product <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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("Marco127/Argu_T2")
# Run inference
sentences = [
    ' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.',
    ' In the event of a disturbance, one polite request (warning) will\nbe given to reduce the noise. If our request is not followed, the guest will be asked to leave\nthe hotel without refund and may be charged Guest Compensation Disturbance Fee.',
    '\nWithout limiting the generality of the aforementioned, it applies to pay-to-view TV programmes or videos, as\nwell as telephone calls or any other expenses of a similar nature that is made from your room, you will be\ndeemed to be the contracting party.',
]
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]

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Evaluation

Metrics

Binary Classification
MetricValue
dot_accuracy0.6746
dotaccuracythreshold49.0201
dot_f10.4933
dotf1threshold35.0242
dot_precision0.3293
dot_recall0.9821
dot_ap0.3294
dot_mcc-0.0392

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Training Details

Training Dataset

Unnamed Dataset
  • —Size: 672 training samples
  • —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
  • —Approximate statistics based on the first 672 samples: | | sentence1 | sentence2 | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 11 tokens</li><li>mean: 48.63 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 48.63 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>0: ~66.67%</li><li>1: ~33.33%</li></ul> |
  • —Samples: | sentence1 | sentence2 | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code><br>The pets can not be left without supervision if there is a risk of causing any<br>damage or might disturb other guests.</code> | <code><br>The pets can not be left without supervision if there is a risk of causing any<br>damage or might disturb other guests.</code> | <code>0</code> | | <code><br>Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these<br>rules are available at the Front Desk upon request.</code> | <code><br>Any guest in violation of these rules may be asked to leave the hotel with no refund. Extra copies of these<br>rules are available at the Front Desk upon request.</code> | <code>0</code> | | <code><br>Consuming the products from the minibar involves additional costs. You can find the<br>prices in the kitchen area.</code> | <code><br>Consuming the products from the minibar involves additional costs. You can find the<br>prices in the kitchen area.</code> | <code>0</code> |
  • —Loss: <code>ContrastiveLoss</code> with these parameters:
json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

Evaluation Dataset

Unnamed Dataset
  • —Size: 169 evaluation samples
  • —Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
  • —Approximate statistics based on the first 169 samples: | | sentence1 | sentence2 | label | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 13 tokens</li><li>mean: 46.01 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 46.01 tokens</li><li>max: 156 tokens</li></ul> | <ul><li>0: ~66.86%</li><li>1: ~33.14%</li></ul> |
  • —Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code><br>I understand and accept that the BON Hotels Group collects the personal information ("personal<br>information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of<br>all in my party, expressly consent and grant permission to the BON Hotels Group to: -<br>collect, collate, process, study and use the personal information; and<br>communicate directly with me/us from time to time, unless I have stated to the contrary below.</code> | <code><br>I understand and accept that the BON Hotels Group collects the personal information ("personal<br>information") of all persons in my party for purposes of loyalty programmes and special offers. I, on behalf of<br>all in my party, expressly consent and grant permission to the BON Hotels Group to: -<br>collect, collate, process, study and use the personal information; and<br>communicate directly with me/us from time to time, unless I have stated to the contrary below.</code> | <code>0</code> | | <code>However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which<br>period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse<br>against us.</code> | <code>However, in lieu of the above, any such goods will only be kept by us for 6 (six) months. At the end of which<br>period, we reserve the right in our sole discretion to dispose thereof and you will have no right of recourse<br>against us.</code> | <code>0</code> | | <code> In cases where the hotel<br>suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it<br>may charge a compensation fee in proportion to the damage. Moral damage may be for<br>example disturbing other guests, thus ruining the reputation of the hotel.</code> | <code> In cases where the hotel<br>suffers damage (either physical, or moral) due to the guests’ violation of the above rules, it<br>may charge a compensation fee in proportion to the damage. Moral damage may be for<br>example disturbing other guests, thus ruining the reputation of the hotel.</code> | <code>0</code> |
  • —Loss: <code>ContrastiveLoss</code> with these parameters:
json
  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

Training Hyperparameters

Non-Default Hyperparameters
  • —eval_strategy: steps
  • —per_device_train_batch_size: 16
  • —per_device_eval_batch_size: 16
  • —num_train_epochs: 5
  • —warmup_ratio: 0.1
  • —fp16: True
  • —batch_sampler: no_duplicates
All Hyperparameters

<details><summary>Click to expand</summary>

  • —overwrite_output_dir: False
  • —do_predict: False
  • —eval_strategy: steps
  • —prediction_loss_only: True
  • —per_device_train_batch_size: 16
  • —per_device_eval_batch_size: 16
  • —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: True
  • —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
  • —fsdp_transformer_layer_cls_to_wrap: None
  • —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: None
  • —hub_always_push: False
  • —gradient_checkpointing: False
  • —gradient_checkpointing_kwargs: None
  • —include_inputs_for_metrics: False
  • —include_for_metrics: []
  • —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
  • —average_tokens_across_devices: False
  • —prompts: None
  • —batch_sampler: no_duplicates
  • —multi_dataset_batch_sampler: proportional

</details>

Training Logs

EpochStepTraining LossValidation Lossdot_ap
-1-1--0.3294
2.33331000.02980.0865-
4.69052000.02410.0865-

Framework Versions

  • —Python: 3.11.11
  • —Sentence Transformers: 3.4.1
  • —Transformers: 4.48.3
  • —PyTorch: 2.5.1+cu124
  • —Accelerate: 1.3.0
  • —Datasets: 3.2.0
  • —Tokenizers: 0.21.0

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",
}
ContrastiveLoss
bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

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