Julien ChaumondandStefan Schweter
ddb6f9476b
Co-Authored-By: Stefan Schweter <stefan-it@users.noreply.github.com>
74 lines
3.8 KiB
Markdown
74 lines
3.8 KiB
Markdown
# 🤗 + 📚 dbmdz BERT models
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In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
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Library open sources Italian BERT models 🎉
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# Italian BERT
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The source data for the Italian BERT model consists of a recent Wikipedia dump and
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various texts from the [OPUS corpora](http://opus.nlpl.eu/) collection. The final
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training corpus has a size of 13GB and 2,050,057,573 tokens.
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For sentence splitting, we use NLTK (faster compared to spacy).
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Our cased and uncased models are training with an initial sequence length of 512
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subwords for ~2-3M steps.
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For the XXL Italian models, we use the same training data from OPUS and extend
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it with data from the Italian part of the [OSCAR corpus](https://traces1.inria.fr/oscar/).
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Thus, the final training corpus has a size of 81GB and 13,138,379,147 tokens.
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## Model weights
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Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers)
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compatible weights are available. If you need access to TensorFlow checkpoints,
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please raise an issue!
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| Model | Downloads
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| --------------------------------------- | ---------------------------------------------------------------------------------------------------------------
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| `dbmdz/bert-base-italian-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/vocab.txt)
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| `dbmdz/bert-base-italian-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/vocab.txt)
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| `dbmdz/bert-base-italian-xxl-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/vocab.txt)
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| `dbmdz/bert-base-italian-xxl-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/vocab.txt)
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## Results
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For results on downstream tasks like NER or PoS tagging, please refer to
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[this repository](https://github.com/stefan-it/fine-tuned-berts-seq).
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## Usage
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With Transformers >= 2.3 our Italian BERT models can be loaded like:
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```python
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-cased")
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model = AutoModel.from_pretrained("dbmdz/bert-base-italian-cased")
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```
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To load the (recommended) Italian XXL BERT models, just use:
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```python
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
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model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased")
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```
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# Huggingface model hub
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All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
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# Contact (Bugs, Feedback, Contribution and more)
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For questions about our BERT models just open an issue
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[here](https://github.com/dbmdz/berts/issues/new) 🤗
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# Acknowledgments
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Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
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Thanks for providing access to the TFRC ❤️
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Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
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it is possible to download both cased and uncased models from their S3 storage 🤗
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