pytorch tf jax bert masked-lm transformers fill-mask
.gitattributes README.txt config.json flax_model.msgpack pytorch_model.bin tf_model.h5 tokenizer.py vocab.txt
:::::README::::::
AlBERTo the first italian BERT model for Twitter languange understanding

Recent scientific studies on natural language processing (NLP) report the outstanding effectiveness observed in the use of context-dependent and task-free language understanding models such as ELMo, GPT, and BERT. Specifically, they have proved to achieve state of the art performance in numerous complex NLP tasks such as question answering and sentiment analysis in the English language. Following the great popularity and effectiveness that these models are gaining in the scientific community, we trained a BERT language understanding model for the Italian language (AlBERTo). In particular, AlBERTo is focused on the language used in social networks, specifically on Twitter. To demonstrate its robustness, we evaluated AlBERTo on the EVALITA 2016 task SENTIPOLC (SENTIment POLarity Classification) obtaining state of the art results in subjectivity, polarity and irony detection on Italian tweets. The pre-trained AlBERTo model will be publicly distributed through the GitHub platform at the following web address: https://github.com/marcopoli/AlBERTo-it in order to facilitate future research.
http://ceur-ws.org/Vol-2481/paper57.pdf

Please cite:
  @InProceedings{PolignanoEtAlCLIC2019,
    author    = {Marco Polignano and Pierpaolo Basile and Marco de Gemmis and Giovanni Semeraro and Valerio Basile},
    title     = {{AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets}},
    booktitle = {Proceedings of the Sixth Italian Conference on Computational Linguistics (CLiC-it 2019)},
    year      = {2019},
    publisher = {CEUR},
    journal={CEUR Workshop Proceedings},
    volume={2481},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074851349&partnerID=40&md5=7abed946e06f76b3825ae5e294ffac14},
    document_type={Conference Paper},
    source={Scopus}
  }

::::CREDITS::::
  Authors: Marco Polignano, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro
           University of Bari ALDO Moro

           Valerio Basile
           University of Turin

  Thanks to: Angelo Basile
             Junior Research Scientist at Symanto - Profiling AI
             for tensorflow and pytorch models compatible with huggingface.co Transformers library


::::COPYRIGHTS::::
# Copyright 2019 Marco Polignano
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
