v1 readme

README.md

@@ -1,3 +1,88 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- ArchitRastogi/Italian-BERT-FineTuning-Embeddings
+language:
+- it
+base_model:
+- dbmdz/bert-base-italian-xxl-uncased
+---
+
+# bert-base-italian-embeddings: A Fine-Tuned Italian BERT Model for IR and RAG Applications
+
+## Model Overview
+
+This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-uncased](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased) tailored for Italian language Information Retrieval (IR) and Retrieval-Augmented Generation (RAG) tasks. It leverages contrastive learning to generate high-quality embeddings suitable for both industry and academic applications.
+
+## Model Size
+
+- **Size**: Approximately 450 MB
+
+## Training Details
+
+- **Base Model**: [dbmdz/bert-base-italian-xxl-uncased](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased)
+- **Dataset**: [Italian-BERT-FineTuning-Embeddings](https://huggingface.co/datasets/ArchitRastogi/Italian-BERT-FineTuning-Embeddings)
+  - Derived from the C4 dataset using sliding window segmentation and in-document sampling.
+  - **Size**: ~5GB (4.5GB train, 0.5GB test)
+- **Training Configuration**:
+  - **Hardware**: NVIDIA A40 GPU
+  - **Epochs**: 3
+  - **Total Steps**: 922,958
+  - **Training Time**: Approximately 5 days, 2 hours, and 23 minutes
+- **Training Objective**: Contrastive Learning
+
+## Evaluation Metrics
+
+Evaluations were performed using the [mMARCO](https://github.com/unicamp-dl/mMARCO) dataset, a multilingual version of MS MARCO. The model was assessed on 6,980 queries.
+
+### Results Comparison
+
+| Metric              | Base Model (`dbmdz/bert-base-italian-xxl-uncased`) | `facebook/mcontriever-msmarco` | **Fine-Tuned Model** |
+|---------------------|----------------------------------------------------|--------------------------------|----------------------|
+| **Recall@1**        | 0.0026                                             | 0.0828                         | **0.2106**           |
+| **Recall@100**      | 0.0417                                             | 0.5028                         | **0.8356**           |
+| **Recall@1000**     | 0.2061                                             | 0.8049                         | **0.9719**           |
+| **Average Precision** | 0.0050                                          | 0.1397                         | **0.3173**           |
+| **NDCG@10**         | 0.0043                                             | 0.1591                         | **0.3601**           |
+| **NDCG@100**        | 0.0108                                             | 0.2086                         | **0.4218**           |
+| **NDCG@1000**       | 0.0299                                             | 0.2454                         | **0.4391**           |
+| **MRR@10**          | 0.0036                                             | 0.1299                         | **0.3047**           |
+| **MRR@100**         | 0.0045                                             | 0.1385                         | **0.3167**           |
+| **MRR@1000**        | 0.0050                                             | 0.1397                         | **0.3173**           |
+
+**Note**: The fine-tuned model significantly outperforms both the base model and `facebook/mcontriever-msmarco` across all metrics.
+
+## Usage
+
+You can load and use the model directly with the Hugging Face Transformers library:
+```python
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("ArchitRastogi/bert-base-italian-embeddings")
+model = AutoModelForMaskedLM.from_pretrained("ArchitRastogi/bert-base-italian-embeddings")
+
+# Example usage
+text = "Stanchi di non riuscire a trovare il partner perfetto?"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+## Intended Use
+This model is intended for:
+
+- Information Retrieval (IR): Enhancing search engines and retrieval systems in the Italian language.
+- Retrieval-Augmented Generation (RAG): Improving the quality of generated content by providing relevant context.
+
+Suitable for both industry applications and academic research.
+
+## Limitations
+- The model may inherit biases present in the C4 dataset.
+- Performance is primarily evaluated on mMARCO; results may vary with other datasets.
+
+---
+
+## Contact
+
+**Archit Rastogi**  
+📧 [email protected]