osiria
/

deberta-base-italian-uncased-ner

+---
+license: mit
+language:
+- it
+widget:
+- text: "mi chiamo marco rossi, vivo a roma e lavoro per l'agenzia spaziale italiana"
+  example_title: "Example 1"
+---
+--------------------------------------------------------------------------------------------------
+<body>
+<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
+<br>
+<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">    Task: Named Entity Recognition</span>
+<br>
+<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">    Model: DeBERTa</span>
+<br>
+<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">    Lang: IT</span>
+<br>
+<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;">  Type: Uncased</span>
+<br>
+<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
+</body>
+--------------------------------------------------------------------------------------------------
+<h3>Model description</h3>
+This is a <b>DeBERTa</b> <b>[1]</b> uncased model for the <b>Italian</b> language, fine-tuned for <b>Named Entity Recognition</b> (<b>Person</b>, <b>Location</b>, <b>Organization</b> and <b>Miscellanea</b> classes) on the [WikiNER](https://figshare.com/articles/dataset/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) dataset <b>[2]</b>, using [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) as a pre-trained model.
+<h3>Training and Performances</h3>
+The model is trained to perform entity recognition over 4 classes: <b>PER</b> (persons), <b>LOC</b> (locations), <b>ORG</b> (organizations), <b>MISC</b> (miscellanea, mainly events, products and services). It has been fine-tuned for Named Entity Recognition, using the WikiNER Italian dataset plus an additional custom dataset of manually annotated Wikipedia paragraphs.
+The WikiNER dataset has been splitted in 102.352 training instances and 25.588 test instances, and the model has been trained for 1 epoch with a constant learning rate of 1e-5.
+The model has been first fine-tuned on WikiNER, then focused on the Italian language and turned to uncased by modifying the embedding layer (as in [3], computing document-level frequencies over the Wikipedia dataset), and lastly fine-tuned on an additional fine-tuning on ~3.500 manually annotated lowercase paragraphs.
+<h3>Quick usage</h3>
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+import re
+import string
+tokenizer = AutoTokenizer.from_pretrained("osiria/deberta-base-italian-uncased-ner")
+model = AutoModelForTokenClassification.from_pretrained("osiria/deberta-base-italian-uncased-ner", num_labels = 5)
+text = "mi chiamo marco rossi, vivo a roma e lavoro per l'agenzia spaziale italiana nella missione prisma"
+for p in string.punctuation:
+    text = text.replace(p, " " + p + " ")
+ner(text)
+[{'entity_group': 'PER',
+  'score': 0.9929622,
+  'word': 'marco rossi',
+  'start': 9,
+  'end': 21},
+ {'entity_group': 'LOC',
+  'score': 0.989851,
+  'word': 'roma',
+  'start': 31,
+  'end': 36},
+ {'entity_group': 'ORG',
+  'score': 0.99059105,
+  'word': 'agenzia spaziale italiana',
+  'start': 53,
+  'end': 79},
+ {'entity_group': 'MISC',
+  'score': 0.9247404,
+  'word': 'missione prisma',
+  'start': 85,
+  'end': 101}]
+```
+<h3>References</h3>
+[1] https://arxiv.org/abs/2111.09543
+[2] https://www.sciencedirect.com/science/article/pii/S0004370212000276
+[3] https://arxiv.org/abs/2010.05609
+<h3>Limitations</h3>
+This model is mainly trained on Wikipedia, so it's particularly suitable for natively digital text from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). However, it may show limitations when it comes to chaotic text, containing errors and slang expressions
+(like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).
+<h3>License</h3>
+The model is released under <b>MIT</b> license