paws-MiniLM-v0.1

CrossEncoder

This is a Cross Encoder model trained on the paws dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• Model Type: Cross Encoder

<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->

• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label
• Training Dataset:
  • paws
• Language: en

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

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("tani-at-nola/paws-MiniLM-v0.1")
# Get scores for pairs of texts
pairs = [
    ['This was a series of nested angular standards , so that measurements in azimuth and elevation could be done directly in polar coordinates relative to the ecliptic .', 'This was a series of nested polar scales , so that measurements in azimuth and elevation could be performed directly in angular coordinates relative to the ecliptic .'],
    ['His father emigrated to Missouri in 1868 but returned when his wife became ill and before the rest of the family could also go to America .', 'His father emigrated to America in 1868 , but returned when his wife became ill and before the rest of the family could go to Missouri .'],
    ['In January 2011 , the Deputy Secretary General of FIBA Asia , Hagop Khajirian , inspected the venue together with SBP - President Manuel V. Pangilinan .', 'In January 2011 , FIBA Asia deputy secretary general Hagop Khajirian along with SBP president Manuel V. Pangilinan inspected the venue .'],
    ['Steiner argued that , in the right circumstances , the spiritual world can be explored through direct experience by practicing ethical and cognitive forms of rigorous self-discipline .', 'Steiner held that the spiritual world can be researched in the right circumstances through direct experience , by persons practicing rigorous forms of ethical and cognitive self-discipline .'],
    ['Luciano Williames Dias ( born July 25 , 1970 ) is a Brazilian football coach and former player .', 'Luciano Williames Dias ( born 25 July 1970 ) is a former football coach and Brazilian player .'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'This was a series of nested angular standards , so that measurements in azimuth and elevation could be done directly in polar coordinates relative to the ecliptic .',
    [
        'This was a series of nested polar scales , so that measurements in azimuth and elevation could be performed directly in angular coordinates relative to the ecliptic .',
        'His father emigrated to America in 1868 , but returned when his wife became ill and before the rest of the family could go to Missouri .',
        'In January 2011 , FIBA Asia deputy secretary general Hagop Khajirian along with SBP president Manuel V. Pangilinan inspected the venue .',
        'Steiner held that the spiritual world can be researched in the right circumstances through direct experience , by persons practicing rigorous forms of ethical and cognitive self-discipline .',
        'Luciano Williames Dias ( born 25 July 1970 ) is a former football coach and Brazilian player .',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

<!-- ### 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

paws

• Dataset: paws at 161ece9
• Size: 49,401 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	<ul><li>min: 29 characters</li><li>mean: 112.38 characters</li><li>max: 202 characters</li></ul>	<ul><li>min: 24 characters</li><li>mean: 112.65 characters</li><li>max: 207 characters</li></ul>	<ul><li>0: ~55.60%</li><li>1: ~44.40%</li></ul>

• Samples:

  sentence1	sentence2	label
  <code>In Paris , in October 1560 , he secretly met the English ambassador , Nicolas Throckmorton , asking him for a passport to return to England through Scotland .</code>	<code>In October 1560 , he secretly met with the English ambassador , Nicolas Throckmorton , in Paris , and asked him for a passport to return to Scotland through England .</code>	<code>0</code>
  <code>The NBA season of 1975 -- 76 was the 30th season of the National Basketball Association .</code>	<code>The 1975 -- 76 season of the National Basketball Association was the 30th season of the NBA .</code>	<code>1</code>
  <code>There are also specific discussions , public profile debates and project discussions .</code>	<code>There are also public discussions , profile specific discussions , and project discussions .</code>	<code>0</code>

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Evaluation Dataset

paws

• Dataset: paws at 161ece9
• Size: 8,000 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	<ul><li>min: 36 characters</li><li>mean: 115.22 characters</li><li>max: 193 characters</li></ul>	<ul><li>min: 35 characters</li><li>mean: 115.21 characters</li><li>max: 193 characters</li></ul>	<ul><li>0: ~56.10%</li><li>1: ~43.90%</li></ul>

• Samples:

  sentence1	sentence2	label
  <code>This was a series of nested angular standards , so that measurements in azimuth and elevation could be done directly in polar coordinates relative to the ecliptic .</code>	<code>This was a series of nested polar scales , so that measurements in azimuth and elevation could be performed directly in angular coordinates relative to the ecliptic .</code>	<code>0</code>
  <code>His father emigrated to Missouri in 1868 but returned when his wife became ill and before the rest of the family could also go to America .</code>	<code>His father emigrated to America in 1868 , but returned when his wife became ill and before the rest of the family could go to Missouri .</code>	<code>0</code>
  <code>In January 2011 , the Deputy Secretary General of FIBA Asia , Hagop Khajirian , inspected the venue together with SBP - President Manuel V. Pangilinan .</code>	<code>In January 2011 , FIBA Asia deputy secretary general Hagop Khajirian along with SBP president Manuel V. Pangilinan inspected the venue .</code>	<code>1</code>

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• 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: 2e-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: {}
• 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: {'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: 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: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.13.5
• Sentence Transformers: 5.0.0
• Transformers: 4.49.0
• PyTorch: 2.6.0
• Accelerate: 1.8.1
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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",
}

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