Add new CrossEncoder model

README.md

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+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:9408
+- loss:CrossEntropyLoss
+datasets:
+- software-si/horeca-recensioni-nli-it
+pipeline_tag: text-classification
+library_name: sentence-transformers
+---
+
+# CrossEncoder
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model trained on the [horeca-recensioni-nli-it](https://huggingface.co/datasets/software-si/horeca-recensioni-nli-it) 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 3 labels
+- **Training Dataset:**
+    - [horeca-recensioni-nli-it](https://huggingface.co/datasets/software-si/horeca-recensioni-nli-it)
+<!-- - **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/horeca-recensioni-ita-nli")
+# Get scores for pairs of texts
+pairs = [
+    [' colazione servita con cura, ampia e confortevole, stanza silenziosa e riposante, stanza polverosa e disordinata,', 'la camera era pulita'],
+    [' camera stretta, non adatta a soggiorni lunghi camera rumorosa e poco confortevole, camera con igiene insufficiente, grande attenzione alle esigenze degli ospiti, vasta scelta che accontenta tutti i gusti,', 'la colazione era buona'],
+    [' colazione abbondante e di qualità, bagno con residui poco igienici, poco spazio per spostarsi liberamente nessun disturbo acustico,', 'la camera era silenziosa'],
+    [' staff disponibile 24 ore su 24, musica proveniente dalle stanze vicine, camera di metratura confortevole,', 'il cliente ha apprezzato la colazione'],
+    [' servizio attento e personalizzato, tutto in ordine e pulito,', 'il livello di pulizia non era soddisfacente'],
+]
+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-recensioni-nli-it
+
+* Dataset: [horeca-recensioni-nli-it](https://huggingface.co/datasets/software-si/horeca-recensioni-nli-it) at [620bed7](https://huggingface.co/datasets/software-si/horeca-recensioni-nli-it/tree/620bed76871c0680963624c74b7cd809adfa3f17)
+* Size: 9,408 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: 52 characters</li><li>mean: 123.01 characters</li><li>max: 204 characters</li></ul> | <ul><li>min: 20 characters</li><li>mean: 31.72 characters</li><li>max: 52 characters</li></ul> | <ul><li>0: ~37.40%</li><li>1: ~35.70%</li><li>2: ~26.90%</li></ul> |
+* Samples:
+  | premises                                                                                                                                                                                                    | hypothesis                                               | labels         |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|:---------------|
+  | <code> poco spazio tra letto e mobili perfetta per chi cerca tranquillità, spazi interni ben mantenuti,</code>                                                                                              | <code>la camera era sporca</code>                        | <code>0</code> |
+  | <code> dolci e salati preparati al momento, camera rumorosa e poco confortevole, non c’era abbastanza spazio per aprire le valigie bagno con residui poco igienici,</code>                                  | <code>la camera è spaziosa e comoda</code>               | <code>0</code> |
+  | <code> totale mancanza di attenzione al cliente, camera trasandata e poco igienica, camera spaziosa e accogliente, frutta fresca e yogurt sempre disponibili, rumori fastidiosi dalle camere vicine,</code> | <code>il cliente è stato soddisfatto del servizio</code> | <code>0</code> |
+* Loss: [<code>CrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss)
+
+### Evaluation Dataset
+
+#### horeca-recensioni-nli-it
+
+* Dataset: [horeca-recensioni-nli-it](https://huggingface.co/datasets/software-si/horeca-recensioni-nli-it) at [620bed7](https://huggingface.co/datasets/software-si/horeca-recensioni-nli-it/tree/620bed76871c0680963624c74b7cd809adfa3f17)
+* Size: 2,822 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: 52 characters</li><li>mean: 122.86 characters</li><li>max: 205 characters</li></ul> | <ul><li>min: 20 characters</li><li>mean: 31.87 characters</li><li>max: 52 characters</li></ul> | <ul><li>0: ~36.30%</li><li>1: ~34.70%</li><li>2: ~29.00%</li></ul> |
+* Samples:
+  | premises                                                                                                                                                                                                                   | hypothesis                            | labels         |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|:---------------|
+  | <code> colazione servita con cura, ampia e confortevole, stanza silenziosa e riposante, stanza polverosa e disordinata,</code>                                                                                             | <code>la camera era pulita</code>     | <code>0</code> |
+  | <code> camera stretta, non adatta a soggiorni lunghi camera rumorosa e poco confortevole, camera con igiene insufficiente, grande attenzione alle esigenze degli ospiti, vasta scelta che accontenta tutti i gusti,</code> | <code>la colazione era buona</code>   | <code>1</code> |
+  | <code> colazione abbondante e di qualità, bagno con residui poco igienici, poco spazio per spostarsi liberamente nessun disturbo acustico,</code>                                                                          | <code>la camera era silenziosa</code> | <code>1</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`: 20
+- `warmup_steps`: 18816
+- `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`: 20
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 18816
+- `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 |
+|:-------:|:----:|:-------------:|:---------------:|
+| 1.3605  | 400  | 1.0237        | 1.0239          |
+| 2.7211  | 800  | 1.022         | 1.0123          |
+| 4.0816  | 1200 | 1.02          | 1.0210          |
+| 5.4422  | 1600 | 1.018         | 1.0173          |
+| 6.8027  | 2000 | 1.0215        | 1.0087          |
+| 8.1633  | 2400 | 1.0143        | 1.0138          |
+| 9.5238  | 2800 | 1.0146        | 1.0137          |
+| 10.8844 | 3200 | 1.0135        | 1.0093          |
+| 12.2449 | 3600 | 1.0134        | 1.0099          |
+| 13.6054 | 4000 | 1.0085        | 1.0072          |
+| 14.9660 | 4400 | 1.0086        | 1.0072          |
+| 16.3265 | 4800 | 1.0046        | 1.0044          |
+
+
+### 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

@@ -0,0 +1,38 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.linear.Identity",
+    "version": "5.1.1"
+  },
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e72cd9692f6417800c032818bb4f81fb0a33c665ce2ef27db1d0387964adad2
+size 439743484

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "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": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}