Update README.md
Browse files
README.md
CHANGED
@@ -6,4 +6,60 @@ tags:
|
|
6 |
- legal
|
7 |
widget:
|
8 |
- text: "Modifica dell' area marina protetta denominata Cinque Terre"
|
9 |
-
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
- legal
|
7 |
widget:
|
8 |
- text: "Modifica dell' area marina protetta denominata Cinque Terre"
|
9 |
+
---
|
10 |
+
# Gulbert-ft-ita
|
11 |
+
|
12 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
13 |
+
|
14 |
+
This model can be used for multi-label classification of Italian legislative acts, according to the subject index (taxonomy) currently adopted in the Gazzetta Uffciale. The model has been obtained by fine-tuning a [BERT-XXL Italian](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased) model on a large corpus of legislative acts published in the Gazzetta Ufficiale from 1988 until early 2022.
|
15 |
+
|
16 |
+
## Model Details
|
17 |
+
|
18 |
+
### Model Description
|
19 |
+
|
20 |
+
<!-- Provide a longer summary of what this model is. -->
|
21 |
+
|
22 |
+
- **Language(s) (NLP):** Italian
|
23 |
+
- **License:** apache-2.0
|
24 |
+
- **Finetuned from model:** https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased
|
25 |
+
|
26 |
+
### Model Sources
|
27 |
+
|
28 |
+
<!-- Provide the basic links for the model. -->
|
29 |
+
|
30 |
+
- **Repository:** https://huggingface.co/dhfbk
|
31 |
+
- **Paper:** M. Rovera, A. Palmero Aprosio, F. Greco, M. Lucchese, S. Tonelli and A. Antetomaso (2023) **Italian Legislative Text Classification for Gazzetta Ufficiale**. In *Proceedings of the Fifth Natural Legal Language Workshop* (NLLP2023).
|
32 |
+
- **Demo:** https://dh-server.fbk.eu/ipzs-ui-demo/
|
33 |
+
|
34 |
+
## Uses
|
35 |
+
|
36 |
+
|
37 |
+
### Direct Use
|
38 |
+
|
39 |
+
Multi-label text classification of Italian legislative acts.
|
40 |
+
|
41 |
+
|
42 |
+
## Training Details
|
43 |
+
|
44 |
+
### Training Data
|
45 |
+
|
46 |
+
The [dataset](https://github.com/dhfbk/gazzetta-ufficiale) used for training the model can be retrieved at our [GitHub account](https://github.com/dhfbk) and is further documented in the above mentioned paper.
|
47 |
+
|
48 |
+
|
49 |
+
|
50 |
+
## Evaluation
|
51 |
+
|
52 |
+
|
53 |
+
### Results
|
54 |
+
|
55 |
+
The model achieves a micro-F1 score of 0.873, macro-F1 of 0.471 and a weighted-F1 of 0.864 on the test set (3-fold average).
|
56 |
+
|
57 |
+
|
58 |
+
|
59 |
+
## Citation
|
60 |
+
|
61 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
62 |
+
|
63 |
+
**BibTeX:**
|
64 |
+
|
65 |
+
TBP, please see above
|