Update README.md

README.md

@@ -10,29 +10,77 @@ base_model: dbmdz/bert-base-italian-uncased
 pipeline_tag: text-classification
 library_name: sentence-transformers
 ---
+# 🍳 Horeca Cucine Industriali – Modello NLI Specializzato (Italiano)
 
-# CrossEncoder based on dbmdz/bert-base-italian-uncased
+## 📌 Panoramica
 
-This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) on the json 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.
+Questo modello NLI (Natural Language Inference) è **il primo modello open-source specializzato esclusivamente sull’analisi semantica di schede tecniche di CUCINE INDUSTRIALI e PIANI COTTURA**.
 
-## Model Details
+✅ Non è un modello generalista   
 
-### Model Description
-- **Model Type:** Cross Encoder
-- **Base model:** [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) <!-- at revision 55058d75cf3bc75a67a412584491b774cb99d68a -->
-- **Maximum Sequence Length:** 512 tokens
-- **Number of Output Labels:** 3 labels
-- **Training Dataset:**
-    - json
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+Il modello è in grado di stabilire se una caratteristica **è presente (entailment), assente (contradiction) o non menzionata (neutral)** nella descrizione tecnica di un prodotto.
 
-### Model Sources
+---
+
+## 🎯 Obiettivo del Modello
+
+Consentire a sistemi di ricerca, Q&A o RAG di:
+
+- Comprendere realmente le schede prodotto di cucine professionali
+- Verificare se una certa caratteristica è presente o meno
+- Estrarre informazioni in modo intelligente tramite logica NLI
+
+---
+
+## ✅ Caratteristiche Analizzate
+
+Il modello è stato addestrato per riconoscere e validare le seguenti caratteristiche:
+
+- Numero di zone di cottura (es. 4 fuochi, 6 fuochi, 4 piastre)
+- Tipo di cottura (gas, elettrico, induzione)
+- Disposizione (su forno, a banco, top)
+- Dimensioni (larghezza, profondità)
+- Struttura / accessori (vano forno, vano aperto, mobile chiuso, alzatina, ecc.)
+
+---
+
+## 💡 Perché è Unico?
+
+| Modello | Specializzazione | Lingua | Applicazione reale |
+|--------|------------------|--------|--------------------|
+| GPT / Llama / generalisti | ❌ No | 🌐 Multi | ❓ Limitata |
+| Classici modelli NLI | ❌ No | ❓ Spesso EN | ❌ Non capiscono il dominio tecnico |
+| **QUESTO MODELLO** | ✅ SOLO cucine e piani cottura | ✅ Italiano | ✅ Pensato per industria Ho.Re.Ca |
+
+---
+
+## 🔧 Architettura
+
+- **Base model:**  `dbmdz/bert-base-italian-uncased`)
+- **Task:** Natural Language Inference (3 classi: entailment, contradiction, neutral)
+- **Linguaggio:** Italiano
+- **Dominio:** Schede tecniche di cucine industriali
+
+---
+
+## 🧾 Formato Input / Output
+
+Questo è un modello NLI: riceve in input **premessa + ipotesi**.
+
+**Esempio:**
+
+- Premessa:  
+  `Cucina a gas 4 fuochi con vano forno statico`
 
-- **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)
+- Ipotesi:  
+  `la cucina ha un forno`
+
+- Output:  
+  `entailment`
+
+---
+
+## 🧪 Esempio d’Uso (Python)
 
 ## Usage
 
@@ -63,42 +111,6 @@ 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.*
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-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-
 ## Training Details
 
 ### Training Dataset
@@ -141,142 +153,6 @@ You can finetune this model on your own dataset.
   | <code>modulo cucina misure 70 cm di profondità, forno alimentato elettricamente, dispone di 6 fuochi,</code>   | <code>la cucina ha un forno</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`: 1
-- `warmup_steps`: 47424
-- `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`: 47424
-- `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
-- `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
-- `project`: huggingface
-- `trackio_space_id`: trackio
-- `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`: no
-- `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`: True
-- `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 |
@@ -315,21 +191,3 @@ You can finetune this model on your own dataset.
     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.*
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-## Model Card Contact
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