Add new CrossEncoder model

README.md

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+---
+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
+---
+
+# 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 -->
+
+### 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)
+```
+
+<!--
+### 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
+
+#### 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`: 32
+- `per_device_eval_batch_size`: 32
+- `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`: 32
+- `per_device_eval_batch_size`: 32
+- `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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.1556 | 500  | 0.2842        | 0.1468          |
+| 0.3111 | 1000 | 0.1083        | 0.0741          |
+| 0.1556 | 500  | 0.0652        | 0.0457          |
+| 0.3111 | 1000 | 0.0303        | 0.0189          |
+| 0.4667 | 1500 | 0.0157        | 0.0357          |
+
+
+### 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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "contradiction",
+    "1": "entailment",
+    "2": "neutral"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "contradiction": 0,
+    "entailment": 1,
+    "neutral": 2
+  },
+  "layer_norm_eps": 1e-07,
+  "legacy": true,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "5.1.1"
+  },
+  "share_att_key": true,
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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+oid sha256:f2e33b79edab81d0c3e4f29a140d91901f89d856ff9f087603d55052c2ee958d
+size 737722356

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128000": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}