[model_cards] UmBERTo
Co-Authored-By: Loreto Parisi <loretoparisi@gmail.com> Co-Authored-By: Simone Francia <francia.simone1@gmail.com>
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# UmBERTo Wikipedia Uncased
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[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at [github.com/huggingface/transformers](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1)
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<p align="center">
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<img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" width="700"> </br>
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Marco Lodola, Monument to Umberto Eco, Alessandria 2019
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</p>
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## Dataset
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UmBERTo-Wikipedia-Uncased Training is trained on a relative small corpus (~7GB) extracted from [Wikipedia-ITA](https://linguatools.org/tools/corpora/wikipedia-monolingual-corpora/).
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## Pre-trained model
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| Model | WWM | Cased | Tokenizer | Vocab Size | Train Steps | Download |
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| ------ | ------ | ------ | ------ | ------ |------ | ------ |
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| `umberto-wikipedia-uncased-v1` | YES | YES | SPM | 32K | 100k | [Link](http://bit.ly/35wbSj6) |
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This model was trained with [SentencePiece](https://github.com/google/sentencepiece) and Whole Word Masking.
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## Downstream Tasks
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These results refers to umberto-wikipedia-uncased model. All details are at [Umberto](https://github.com/musixmatchresearch/umberto) Official Page.
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#### Named Entity Recognition (NER)
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| Dataset | F1 | Precision | Recall | Accuracy |
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| ------ | ------ | ------ | ------ | ----- |
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| **ICAB-EvalITA07** | **86.240** | 85.939 | 86.544 | 98.534 |
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| **WikiNER-ITA** | **90.483** | 90.328 | 90.638 | 98.661 |
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#### Part of Speech (POS)
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| Dataset | F1 | Precision | Recall | Accuracy |
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| ------ | ------ | ------ | ------ | ------ |
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| **UD_Italian-ISDT** | 98.563 | 98.508 | 98.618 | **98.717** |
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| **UD_Italian-ParTUT** | 97.810 | 97.835 | 97.784 | **98.060** |
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## Usage
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##### Load UmBERTo Wikipedia Uncased with AutoModel, Autotokenizer:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModel
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tokenizer = AutoTokenizer.from_pretrained("Musixmatch/umberto-wikipedia-uncased-v1")
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umberto = AutoModel.from_pretrained("Musixmatch/umberto-wikipedia-uncased-v1")
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encoded_input = tokenizer.encode("Umberto Eco è stato un grande scrittore")
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input_ids = torch.tensor(encoded_input).unsqueeze(0) # Batch size 1
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outputs = umberto(input_ids)
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last_hidden_states = outputs[0] # The last hidden-state is the first element of the output
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```
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##### Predict masked token:
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```python
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from transformers import pipeline
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fill_mask = pipeline(
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"fill-mask",
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model="Musixmatch/umberto-wikipedia-uncased-v1",
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tokenizer="Musixmatch/umberto-wikipedia-uncased-v1"
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)
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result = fill_mask("Umberto Eco è <mask> un grande scrittore")
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# {'sequence': '<s> umberto eco è stato un grande scrittore</s>', 'score': 0.5784581303596497, 'token': 361}
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# {'sequence': '<s> umberto eco è anche un grande scrittore</s>', 'score': 0.33813193440437317, 'token': 269}
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# {'sequence': '<s> umberto eco è considerato un grande scrittore</s>', 'score': 0.027196012437343597, 'token': 3236}
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# {'sequence': '<s> umberto eco è diventato un grande scrittore</s>', 'score': 0.013716378249228, 'token': 5742}
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# {'sequence': '<s> umberto eco è inoltre un grande scrittore</s>', 'score': 0.010662357322871685, 'token': 1030}
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```
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## Citation
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All of the original datasets are publicly available or were released with the owners' grant. The datasets are all released under a CC0 or CCBY license.
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* UD Italian-ISDT Dataset [Github](https://github.com/UniversalDependencies/UD_Italian-ISDT)
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* UD Italian-ParTUT Dataset [Github](https://github.com/UniversalDependencies/UD_Italian-ParTUT)
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* I-CAB (Italian Content Annotation Bank), EvalITA [Page](http://www.evalita.it/)
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* WIKINER [Page](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) , [Paper](https://www.sciencedirect.com/science/article/pii/S0004370212000276?via%3Dihub)
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```
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@inproceedings {magnini2006annotazione,
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title = {Annotazione di contenuti concettuali in un corpus italiano: I - CAB},
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author = {Magnini,Bernardo and Cappelli,Amedeo and Pianta,Emanuele and Speranza,Manuela and Bartalesi Lenzi,V and Sprugnoli,Rachele and Romano,Lorenza and Girardi,Christian and Negri,Matteo},
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booktitle = {Proc.of SILFI 2006},
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year = {2006}
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}
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@inproceedings {magnini2006cab,
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title = {I - CAB: the Italian Content Annotation Bank.},
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author = {Magnini,Bernardo and Pianta,Emanuele and Girardi,Christian and Negri,Matteo and Romano,Lorenza and Speranza,Manuela and Lenzi,Valentina Bartalesi and Sprugnoli,Rachele},
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booktitle = {LREC},
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pages = {963--968},
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year = {2006},
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organization = {Citeseer}
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}
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```
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## Authors
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**Loreto Parisi**: `loreto at musixmatch dot com`, [loretoparisi](https://github.com/loretoparisi)<br>
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**Simone Francia**: `simone.francia at musixmatch dot com`, [simonefrancia](https://github.com/simonefrancia)<br>
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**Paolo Magnani**: `paul.magnani95 at gmail dot com`, [paulthemagno](https://github.com/paulthemagno)<br>
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## About Musixmatch AI
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<br>
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We do Machine Learning and Artificial Intelligence @[musixmatch](https://twitter.com/Musixmatch)<br>
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Follow us on [Twitter](https://twitter.com/musixmatchai) [Github](https://github.com/musixmatchresearch)
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