tani-at-nola/reranker-deberta-v3-base-nli

CrossEncoder based on microsoft/deberta-v3-base

This is a Cross Encoder model finetuned from microsoft/deberta-v3-base 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: microsoft/deberta-v3-base
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label

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/reranker-deberta-v3-base-nli")
# Get scores for pairs of texts
pairs = [
    ['The sisters are hugging goodbye while holding to go packages after just eating lunch.', 'Two women are embracing while holding to go packages.'],
    ['Two woman are holding packages.', 'Two women are embracing while holding to go packages.'],
    ['The men are fighting outside a deli.', 'Two women are embracing while holding to go packages.'],
    ['Two kids in numbered jerseys wash their hands.', 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.'],
    ['Two kids at a ballgame wash their hands.', 'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'The sisters are hugging goodbye while holding to go packages after just eating lunch.',
    [
        'Two women are embracing while holding to go packages.',
        'Two women are embracing while holding to go packages.',
        'Two women are embracing while holding to go packages.',
        'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.',
        'Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

• Datasets: AllNLI-norm-dev and AllNLI-test
• Evaluated with CrossEncoderClassificationEvaluator

Metric	AllNLI-norm-dev	AllNLI-test
accuracy	0.6807	0.6814
accuracy_threshold	0.4376	0.56
f1	0.5466	0.527
f1_threshold	0.0044	0.001
precision	0.4004	0.3655
recall	0.861	0.9436
average_precision	0.4993	0.4819

Training Details

Training Dataset

Unnamed Dataset

• Size: 942,069 training samples
• Columns: hypothesis, premise, and label
• Approximate statistics based on the first 1000 samples:

  	hypothesis	premise	label
  type	string	string	int
  details	min: 11 characters mean: 38.26 characters max: 131 characters	min: 23 characters mean: 69.54 characters max: 227 characters	0: ~66.60% 1: ~33.40%

• Samples:

  hypothesis	premise	label
  A person is training his horse for a competition.	A person on a horse jumps over a broken down airplane.	0
  A person is at a diner, ordering an omelette.	A person on a horse jumps over a broken down airplane.	0
  A person is outdoors, on a horse.	A person on a horse jumps over a broken down airplane.	1

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

• Size: 19,657 evaluation samples
• Columns: hypothesis, premise, and label
• Approximate statistics based on the first 1000 samples:

  	hypothesis	premise	label
  type	string	string	int
  details	min: 11 characters mean: 37.66 characters max: 116 characters	min: 16 characters mean: 75.01 characters max: 229 characters	0: ~66.90% 1: ~33.10%

• Samples:

  hypothesis	premise	label
  The sisters are hugging goodbye while holding to go packages after just eating lunch.	Two women are embracing while holding to go packages.	0
  Two woman are holding packages.	Two women are embracing while holding to go packages.	1
  The men are fighting outside a deli.	Two women are embracing while holding to go packages.	0

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• num_train_epochs: 5
• warmup_ratio: 0.1
• bf16: True
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• 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: True
• fp16: False
• 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: True
• 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: True
• 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
• hub_revision: None
• 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
• 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
• liger_kernel_config: None
• 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: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	AllNLI-norm-dev_average_precision	AllNLI-test_average_precision
-1	-1	-	-	0.3614	-
0.0068	100	0.7205	-	-	-
0.0136	200	0.6972	-	-	-
0.0204	300	0.6086	-	-	-
0.0272	400	0.4855	-	-	-
0.0340	500	0.3991	-	-	-
0.0408	600	0.3409	-	-	-
0.0476	700	0.2987	-	-	-
0.0544	800	0.2841	-	-	-
0.0611	900	0.2729	-	-	-
0.0679	1000	0.2627	-	-	-
0.0747	1100	0.2517	-	-	-
0.0815	1200	0.2286	-	-	-
0.0883	1300	0.2385	-	-	-
0.0951	1400	0.2329	-	-	-
0.1019	1500	0.2213	0.1959	0.4997	-
0.1087	1600	0.22	-	-	-
0.1155	1700	0.2295	-	-	-
0.1223	1800	0.2236	-	-	-
0.1291	1900	0.2273	-	-	-
0.1359	2000	0.2071	-	-	-
0.1427	2100	0.2254	-	-	-
0.1495	2200	0.2217	-	-	-
0.1563	2300	0.2093	-	-	-
0.1631	2400	0.2112	-	-	-
0.1698	2500	0.2176	-	-	-
0.1766	2600	0.2195	-	-	-
0.1834	2700	0.2107	-	-	-
0.1902	2800	0.2164	-	-	-
0.1970	2900	0.213	-	-	-
0.2038	3000	0.2055	0.1726	0.4789	-
0.2106	3100	0.2039	-	-	-
0.2174	3200	0.2157	-	-	-
0.2242	3300	0.2155	-	-	-
0.2310	3400	0.2017	-	-	-
0.2378	3500	0.2068	-	-	-
0.2446	3600	0.2111	-	-	-
0.2514	3700	0.2062	-	-	-
0.2582	3800	0.2062	-	-	-
0.2650	3900	0.2217	-	-	-
0.2718	4000	0.2012	-	-	-
0.2786	4100	0.2127	-	-	-
0.2853	4200	0.212	-	-	-
0.2921	4300	0.2075	-	-	-
0.2989	4400	0.2099	-	-	-
0.3057	4500	0.2134	0.1644	0.4993	-
-1	-1	-	-	-	0.4819

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 5.0.0
• Transformers: 4.53.2
• PyTorch: 2.7.1+cu126
• Accelerate: 1.9.0
• Datasets: 4.0.0
• Tokenizers: 0.21.2

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",
}