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software-si/kitchen-nli

sourceHugging Faceapache-2.0updated 11mo agoView on Hugging Face
1likes19downloads
Model Card

CrossEncoder based on cross-encoder/nli-deberta-v3-base

This is a Cross Encoder model finetuned from cross-encoder/nli-deberta-v3-base on the horeca-nli dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text pair classification.

Model Details

Model Description

  • —Model Type: Cross Encoder
  • —Base model: cross-encoder/nli-deberta-v3-base <!-- at revision 6c749ce3425cd33b46d187e45b92bbf96ee12ec7 -->
  • —Maximum Sequence Length: 512 tokens
  • —Number of Output Labels: 3 labels
  • —Training Dataset:
  • —horeca-nli <!-- - Language: Unknown --> <!-- - License: Unknown -->

🧾 Input / Output

This a model for Natural Language Inference NLI. it take a premises and an hypothesis as input, and return a classification of the relationship between the two input sentence Possible outputs are: contradiction, entailment, neutral

Example:

  • —premises: kitchen eighty centimeters wide, deep 70 cm placed on closed compartment
  • —hypothesis: the kitchen is placed on open shelf
  • —Output: contradiction

Model Sources

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 CrossEncoder


# Download from the 🤗 Hub
model = CrossEncoder("software-si/kitchen-nli")
# Get scores for pairs of texts
pairs = [
    ['cooking unit with square plates on compartment with doors', 'the depth of the kitchen is 70 centimeters'],
    ['cooking unit with 2 electric plates, on compartment with doors', 'the kitchen is placed on top'],
    ['kitchen module in top version deep 70 cm eighty centimeters wide,', 'the kitchen is placed on cabinet'],
    ['cooking unit wide 80 cm, with a depth of 90 centimeters, placed on closed compartment', 'the kitchen has a width of 40 cm'],
    ['kitchen with gas cooking, with gas oven, one hundred twenty centimeters wide,', 'the layout of the kitchen is top'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5, 3)
label_mapping = ['contradiction', 'entailment', 'neutral']

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

horeca-nli
  • —Dataset: horeca-nli at a6bd6a4
  • —Size: 102,836 training samples
  • —Columns: <code>premises</code>, <code>hypothesis</code>, and <code>labels</code>
  • —Approximate statistics based on the first 1000 samples: | | premises | hypothesis | labels | |:--------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 26 characters</li><li>mean: 64.84 characters</li><li>max: 112 characters</li></ul> | <ul><li>min: 23 characters</li><li>mean: 36.55 characters</li><li>max: 60 characters</li></ul> | <ul><li>0: ~33.30%</li><li>1: ~23.70%</li><li>2: ~43.00%</li></ul> |
  • —Samples: | premises | hypothesis | labels | |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:---------------| | <code>kitchen eighty centimeters wide, deep 70 cm placed on closed compartment</code> | <code>the kitchen is forty centimeters wide</code> | <code>0</code> | | <code>cooking unit placed on cabinet deep 90 cm, gas supply,</code> | <code>the kitchen is placed on open shelf</code> | <code>2</code> | | <code>cooking unit wide 40 cm, powered by electricity with the square plates</code> | <code>the kitchen measures one hundred twenty centimeters in width</code> | <code>0</code> |
  • —Loss: <code>CrossEntropyLoss</code>

Evaluation Dataset

horeca-nli
  • —Dataset: horeca-nli at a6bd6a4
  • —Size: 30,851 evaluation samples
  • —Columns: <code>premises</code>, <code>hypothesis</code>, and <code>labels</code>
  • —Approximate statistics based on the first 1000 samples: | | premises | hypothesis | labels | |:--------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 21 characters</li><li>mean: 65.62 characters</li><li>max: 114 characters</li></ul> | <ul><li>min: 23 characters</li><li>mean: 36.56 characters</li><li>max: 60 characters</li></ul> | <ul><li>0: ~35.20%</li><li>1: ~23.20%</li><li>2: ~41.60%</li></ul> |
  • —Samples: | premises | hypothesis | labels | |:-------------------------------------------------------------------------------|:--------------------------------------------------------|:---------------| | <code>cooking unit with square plates on compartment with doors</code> | <code>the depth of the kitchen is 70 centimeters</code> | <code>2</code> | | <code>cooking unit with 2 electric plates, on compartment with doors</code> | <code>the kitchen is placed on top</code> | <code>2</code> | | <code>kitchen module in top version deep 70 cm eighty centimeters wide,</code> | <code>the kitchen is placed on cabinet</code> | <code>0</code> |
  • —Loss: <code>CrossEntropyLoss</code>

Training Hyperparameters

Non-Default Hyperparameters
  • —eval_strategy: steps
  • —per_device_train_batch_size: 64
  • —per_device_eval_batch_size: 64
  • —learning_rate: 1e-05
  • —num_train_epochs: 1
  • —warmup_steps: 10283
  • —bf16: True
  • —load_best_model_at_end: True
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: 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: 1e-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: 0.0
  • —warmup_steps: 10283
  • —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: 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: True
  • —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}
  • —parallelism_config: None
  • —deepspeed: None
  • —label_smoothing_factor: 0.0
  • —optim: adamwtorchfused
  • —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: {}

</details>

Training Logs

EpochStepTraining LossValidation Loss
0.31115000.00820.0072
0.622310000.00430.0027
0.933415000.00410.0388
  • —The bold row denotes the saved checkpoint.

Framework Versions

  • —Python: 3.12.11
  • —Sentence Transformers: 5.1.1
  • —Transformers: 4.56.2
  • —PyTorch: 2.8.0+cu128
  • —Accelerate: 1.10.1
  • —Datasets: 4.1.1
  • —Tokenizers: 0.22.1

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",
}

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