Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.6d5147c
-
citazione corretta
2322fc3 -
Update README.md
e9b5a38
-
Update README.md
010f8d8
-
Update README.md
5fd59ad -
Update README.md
30734f4
-
Update README.md
beb576f
-
Update README.md
ec58cea -
Update README.md
74ea0d0 -
Update README.md
565bdef -
Update README.md
5fbb994 -
Update README.md
72f31af -
Upload bureauberto.jpg
fd63a59 -
Delete bureauberto.jpg
e57c62f -
Update README.md
c7e52e2 -
Update README.md
0b3ee43 -
Upload bureauberto.jpg
ccf0e05 -
Update README.md
d3561f1 -
Model Uploaded
ba7af0d -
Update README.md
BureauBERTo is the first transformer-based language model adapted to the Italian Public Administration (PA) and technical-bureaucratic domains. This model results from a further pre-training applied to the general-purpose Italian model UmBERTo. ## Training Corpus BureauBERTo is trained on the Bureau Corpus. , a composite corpus containing PA, banking, and insurance documents. The Bureau Corpus contains 35,293,226 sentences and approximately 1B tokens, for a total amount of 6.7 GB of plain text. The input dataset is constructed by applying the BureauBERTo tokenizer to contiguous sentences from one or more documents, using the separating special token after each sentence. The BureauBERTo vocabulary is expanded with 8,305 domain-specific tokens extracted from the Bureau Corpus. ## Training Procedure The further pre-training is applied with a MLM objective (randomly masking 15\% of the tokens) on the Bureau Corpus. The model was trained for 40 epochs, resulting in 17,400 steps with a b…b0687c9 -
initial commit
9ac4303
