---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:102836
- loss:CrossEntropyLoss
base_model: cross-encoder/nli-deberta-v3-base
datasets:
- software-si/horeca-nli
pipeline_tag: text-classification
library_name: sentence-transformers
license: apache-2.0
language:
- en
---

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

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/nli-deberta-v3-base](https://huggingface.co/cross-encoder/nli-deberta-v3-base) on the [horeca-nli](https://huggingface.co/datasets/software-si/horeca-nli) dataset using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/cross-encoder/nli-deberta-v3-base) <!-- at revision 6c749ce3425cd33b46d187e45b92bbf96ee12ec7 -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 3 labels
- **Training Dataset:**
    - [horeca-nli](https://huggingface.co/datasets/software-si/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

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)

## 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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You can finetune this model on your own dataset.

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## Training Details

### Training Dataset

#### horeca-nli

* Dataset: [horeca-nli](https://huggingface.co/datasets/software-si/horeca-nli) at [a6bd6a4](https://huggingface.co/datasets/software-si/horeca-nli/tree/a6bd6a4e3cfa88c4081a4a0ff814f92d00dcf463)
* 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>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss)

### Evaluation Dataset

#### horeca-nli

* Dataset: [horeca-nli](https://huggingface.co/datasets/software-si/horeca-nli) at [a6bd6a4](https://huggingface.co/datasets/software-si/horeca-nli/tree/a6bd6a4e3cfa88c4081a4a0ff814f92d00dcf463)
* 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>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#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
<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`: {'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`: {}

</details>

### 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",
}
```

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