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
Maximum Sequence Length: 512 tokens
Number of Output Labels: 3 labels
Training Dataset:
- horeca-nli

🧾 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

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.

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']

Training Details

Training Dataset

horeca-nli

Dataset: horeca-nli at a6bd6a4
Size: 102,836 training samples
Columns: premises, hypothesis, and labels
Approximate statistics based on the first 1000 samples:
premises hypothesis labels
type string string int
details
min: 26 characters
mean: 64.84 characters
max: 112 characters
min: 23 characters
mean: 36.55 characters
max: 60 characters
0: ~33.30%
1: ~23.70%
2: ~43.00%

Samples:

premises	hypothesis	labels
`kitchen eighty centimeters wide, deep 70 cm placed on closed compartment`	`the kitchen is forty centimeters wide`	`0`
`cooking unit placed on cabinet deep 90 cm, gas supply,`	`the kitchen is placed on open shelf`	`2`
`cooking unit wide 40 cm, powered by electricity with the square plates`	`the kitchen measures one hundred twenty centimeters in width`	`0`

Loss: CrossEntropyLoss

Evaluation Dataset

horeca-nli

Dataset: horeca-nli at a6bd6a4
Size: 30,851 evaluation samples
Columns: premises, hypothesis, and labels
Approximate statistics based on the first 1000 samples:
premises hypothesis labels
type string string int
details
min: 21 characters
mean: 65.62 characters
max: 114 characters
min: 23 characters
mean: 36.56 characters
max: 60 characters
0: ~35.20%
1: ~23.20%
2: ~41.60%

Samples:

premises	hypothesis	labels
`cooking unit with square plates on compartment with doors`	`the depth of the kitchen is 70 centimeters`	`2`
`cooking unit with 2 electric plates, on compartment with doors`	`the kitchen is placed on top`	`2`
`kitchen module in top version deep 70 cm eighty centimeters wide,`	`the kitchen is placed on cabinet`	`0`

Loss: CrossEntropyLoss

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

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: 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: {'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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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
0.3111	500	0.0082	0.0072
0.6223	1000	0.0043	0.0027
0.9334	1500	0.0041	0.0388

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",
}

	premises	hypothesis	labels
type	string	string	int
details	min: 26 characters mean: 64.84 characters max: 112 characters	min: 23 characters mean: 36.55 characters max: 60 characters	0: ~33.30% 1: ~23.70% 2: ~43.00%