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Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/dbmdz/bert-base-italian-cased/README.md

README.md

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+---
+language: it
+license: mit
+datasets:
+- wikipedia
+---
+
+# 🤗 + 📚 dbmdz BERT and ELECTRA models
+
+In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
+Library open sources Italian BERT and ELECTRA models 🎉
+
+# Italian BERT
+
+The source data for the Italian BERT model consists of a recent Wikipedia dump and
+various texts from the [OPUS corpora](http://opus.nlpl.eu/) collection. The final
+training corpus has a size of 13GB and 2,050,057,573 tokens.
+
+For sentence splitting, we use NLTK (faster compared to spacy).
+Our cased and uncased models are training with an initial sequence length of 512
+subwords for ~2-3M steps.
+
+For the XXL Italian models, we use the same training data from OPUS and extend
+it with data from the Italian part of the [OSCAR corpus](https://traces1.inria.fr/oscar/).
+Thus, the final training corpus has a size of 81GB and 13,138,379,147 tokens.
+
+Note: Unfortunately, a wrong vocab size was used when training the XXL models.
+This explains the mismatch of the "real" vocab size of 31102, compared to the
+vocab size specified in `config.json`. However, the model is working and all
+evaluations were done under those circumstances.
+See [this issue](https://github.com/dbmdz/berts/issues/7) for more information.
+
+The Italian ELECTRA model was trained on the "XXL" corpus for 1M steps in total using a batch
+size of 128. We pretty much following the ELECTRA training procedure as used for
+[BERTurk](https://github.com/stefan-it/turkish-bert/tree/master/electra).
+
+## Model weights
+
+Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers)
+compatible weights are available. If you need access to TensorFlow checkpoints,
+please raise an issue!
+
+| Model                                                | Downloads
+| ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------
+| `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)
+| `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)
+| `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)
+| `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)
+| `dbmdz/electra-base-italian-xxl-cased-discriminator` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/vocab.txt)
+| `dbmdz/electra-base-italian-xxl-cased-generator`     | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-generator/config.json)     • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/pytorch_model.bin)     • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/vocab.txt)
+
+## Results
+
+For results on downstream tasks like NER or PoS tagging, please refer to
+[this repository](https://github.com/stefan-it/italian-bertelectra).
+
+## Usage
+
+With Transformers >= 2.3 our Italian BERT models can be loaded like:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "dbmdz/bert-base-italian-cased"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModel.from_pretrained(model_name)
+```
+
+To load the (recommended) Italian XXL BERT models, just use:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "dbmdz/bert-base-italian-xxl-cased"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModel.from_pretrained(model_name)
+```
+
+To load the Italian XXL ELECTRA model (discriminator), just use:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "dbmdz/electra-base-italian-xxl-cased-discriminator"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelWithLMHead.from_pretrained(model_name)
+```
+
+# Huggingface model hub
+
+All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
+
+# Contact (Bugs, Feedback, Contribution and more)
+
+For questions about our BERT/ELECTRA models just open an issue
+[here](https://github.com/dbmdz/berts/issues/new) 🤗
+
+# Acknowledgments
+
+Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
+Thanks for providing access to the TFRC ❤️
+
+Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
+it is possible to download both cased and uncased models from their S3 storage 🤗