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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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- question-answering |
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language: |
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- it |
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tags: |
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- italian |
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- embeddings |
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- bert |
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- fine-tuning |
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- information-retrieval |
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- semantic-search |
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- natural-language-processing |
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- dense-retrieval |
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- c4-dataset |
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pretty_name: Fine-Tuned BERT for Italian Embeddings |
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size_categories: |
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- 10M<n<100M |
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--- |
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# Italian-BERT-FineTuning-Embeddings |
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This repository contains a comprehensive dataset designed for fine-tuning BERT-based Italian embedding models. The dataset aims to enhance performance on tasks such as **information retrieval**, **semantic search**, and **embeddings generation**. |
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## Dataset Overview |
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This dataset leverages the **C4 dataset** (Italian subset) and employs advanced techniques like **sliding window segmentation** and **in-document sampling** to create high-quality, diverse examples from large Italian documents. |
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### Data Format |
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The dataset is stored in `.jsonl` format with the following fields: |
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- `query`: A query or sentence fragment. |
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- `positive`: A relevant text segment closely associated with the query. |
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- `hard_negative`: A challenging non-relevant text segment, similar in context but unrelated to the query. |
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#### Example: |
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```json |
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{ |
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"query": "Stanchi di non riuscire a trovare il partner perfetto?.", |
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"positive": "La cosa principale da fare è pubblicare il proprio annuncio e aspettare una risposta.", |
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"hard_negative": "Quale rapporto tra investimenti IT e sicurezza?" |
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} |
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``` |
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### Dataset Statistics |
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- **Training Set**: 1.13 million rows (~4.5 GB) |
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- **Test Set**: 9.09 million rows (~0.5 GB) |
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### Dataset Construction |
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This dataset was built using the following methodologies: |
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1. **Sliding Window Segmentation** |
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Extracting overlapping text segments to preserve contextual information and maximize coverage of the source material. |
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2. **In-Document Sampling** |
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Sampling relevant (`positive`) and challenging non-relevant (`hard_negative`) examples within the same document to ensure robust and meaningful examples. |
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**Why C4?** |
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The C4 dataset was selected due to its vast collection of high-quality Italian text, providing a rich source for creating varied training samples. |
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## Fine-Tuned Model |
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A fine-tuned BERT-based Italian embedding model trained on this dataset is available: |
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**[Fine-Tuned Model Repository](<will-be-added>)** |
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### Model Base: |
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- **[dbmdz/bert-base-italian-xxl-uncased](<https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased>)** |
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## Licensing and Usage |
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This dataset is licensed under the **Apache 2.0 License**. If you use this dataset or the fine-tuned model in your research or applications, please provide appropriate credit: |
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> **Archit Rastogi** |
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> Email: [[email protected]](mailto:[email protected]) |
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## Contact |
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For any questions, feedback, or collaboration inquiries, feel free to reach out: |
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**Archit Rastogi** |
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Email: [[email protected]](mailto:[email protected]) |
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Feel free to suggest improvements. Your feedback is highly appreciated! |
