Datasets:
File size: 3,054 Bytes
b7b7aff c251e77 b7b7aff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
license: apache-2.0
task_categories:
- text-classification
- question-answering
language:
- it
tags:
- italian
- embeddings
- bert
- fine-tuning
- information-retrieval
- semantic-search
- natural-language-processing
- dense-retrieval
- c4-dataset
pretty_name: Fine-Tuned BERT for Italian Embeddings
size_categories:
- 10M<n<100M
---
# Italian-BERT-FineTuning-Embeddings
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**.
---
## Dataset Overview
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.
### Data Format
The dataset is stored in `.jsonl` format with the following fields:
- `query`: A query or sentence fragment.
- `positive`: A relevant text segment closely associated with the query.
- `hard_negative`: A challenging non-relevant text segment, similar in context but unrelated to the query.
#### Example:
```json
{
"query": "Stanchi di non riuscire a trovare il partner perfetto?.",
"positive": "La cosa principale da fare è pubblicare il proprio annuncio e aspettare una risposta.",
"hard_negative": "Quale rapporto tra investimenti IT e sicurezza?"
}
```
---
### Dataset Statistics
- **Training Set**: 1.13 million rows (~4.5 GB)
- **Test Set**: 9.09 million rows (~0.5 GB)
---
### Dataset Construction
This dataset was built using the following methodologies:
1. **Sliding Window Segmentation**
Extracting overlapping text segments to preserve contextual information and maximize coverage of the source material.
2. **In-Document Sampling**
Sampling relevant (`positive`) and challenging non-relevant (`hard_negative`) examples within the same document to ensure robust and meaningful examples.
**Why C4?**
The C4 dataset was selected due to its vast collection of high-quality Italian text, providing a rich source for creating varied training samples.
---
## Fine-Tuned Model
A fine-tuned BERT-based Italian embedding model trained on this dataset is available:
**[Fine-Tuned Model Repository](<will-be-added>)**
### Model Base:
- **[dbmdz/bert-base-italian-xxl-uncased](<https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased>)**
---
## Licensing and Usage
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:
> **Archit Rastogi**
> Email: [[email protected]](mailto:[email protected])
---
## Contact
For any questions, feedback, or collaboration inquiries, feel free to reach out:
**Archit Rastogi**
Email: [[email protected]](mailto:[email protected])
---
Feel free to suggest improvements. Your feedback is highly appreciated! |