From ddb6f9476b58ed9bf4433622ca9aa49932929bc0 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Fri, 31 Jan 2020 17:12:39 -0500 Subject: [PATCH] [model_cards] dbmdz models Co-Authored-By: Stefan Schweter --- .../dbmdz/bert-base-german-cased/README.md | 66 +++++++++++++++++ .../dbmdz/bert-base-german-uncased/README.md | 66 +++++++++++++++++ .../dbmdz/bert-base-italian-cased/README.md | 73 +++++++++++++++++++ .../dbmdz/bert-base-italian-uncased/README.md | 73 +++++++++++++++++++ .../bert-base-italian-xxl-cased/README.md | 73 +++++++++++++++++++ .../bert-base-italian-xxl-uncased/README.md | 73 +++++++++++++++++++ 6 files changed, 424 insertions(+) create mode 100644 model_cards/dbmdz/bert-base-german-cased/README.md create mode 100644 model_cards/dbmdz/bert-base-german-uncased/README.md create mode 100644 model_cards/dbmdz/bert-base-italian-cased/README.md create mode 100644 model_cards/dbmdz/bert-base-italian-uncased/README.md create mode 100644 model_cards/dbmdz/bert-base-italian-xxl-cased/README.md create mode 100644 model_cards/dbmdz/bert-base-italian-xxl-uncased/README.md diff --git a/model_cards/dbmdz/bert-base-german-cased/README.md b/model_cards/dbmdz/bert-base-german-cased/README.md new file mode 100644 index 0000000000..fccd050545 --- /dev/null +++ b/model_cards/dbmdz/bert-base-german-cased/README.md @@ -0,0 +1,66 @@ +# 🤗 + 📚 dbmdz German BERT models + +In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State +Library open sources another German BERT models 🎉 + +# German BERT + +## Stats + +In addition to the recently released [German BERT](https://deepset.ai/german-bert) +model by [deepset](https://deepset.ai/) we provide another German-language model. + +The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, +Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with +a size of 16GB and 2,350,234,427 tokens. + +For sentence splitting, we use [spacy](https://spacy.io/). Our preprocessing steps +(sentence piece model for vocab generation) follow those used for training +[SciBERT](https://github.com/allenai/scibert). The model is trained with an initial +sequence length of 512 subwords and was performed for 1.5M steps. + +This release includes both cased and uncased models. + +## 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 +| -------------------------------- | --------------------------------------------------------------------------------------------------------------- +| `bert-base-german-dbmdz-cased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt) +| `bert-base-german-dbmdz-uncased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt) + +## Usage + +With Transformers >= 2.3 our German BERT models can be loaded like: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased") +``` + +## Results + +For results on downstream tasks like NER or PoS tagging, please refer to +[this repository](https://github.com/stefan-it/fine-tuned-berts-seq). + +# 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 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 🤗 diff --git a/model_cards/dbmdz/bert-base-german-uncased/README.md b/model_cards/dbmdz/bert-base-german-uncased/README.md new file mode 100644 index 0000000000..fccd050545 --- /dev/null +++ b/model_cards/dbmdz/bert-base-german-uncased/README.md @@ -0,0 +1,66 @@ +# 🤗 + 📚 dbmdz German BERT models + +In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State +Library open sources another German BERT models 🎉 + +# German BERT + +## Stats + +In addition to the recently released [German BERT](https://deepset.ai/german-bert) +model by [deepset](https://deepset.ai/) we provide another German-language model. + +The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, +Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with +a size of 16GB and 2,350,234,427 tokens. + +For sentence splitting, we use [spacy](https://spacy.io/). Our preprocessing steps +(sentence piece model for vocab generation) follow those used for training +[SciBERT](https://github.com/allenai/scibert). The model is trained with an initial +sequence length of 512 subwords and was performed for 1.5M steps. + +This release includes both cased and uncased models. + +## 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 +| -------------------------------- | --------------------------------------------------------------------------------------------------------------- +| `bert-base-german-dbmdz-cased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt) +| `bert-base-german-dbmdz-uncased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt) + +## Usage + +With Transformers >= 2.3 our German BERT models can be loaded like: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased") +``` + +## Results + +For results on downstream tasks like NER or PoS tagging, please refer to +[this repository](https://github.com/stefan-it/fine-tuned-berts-seq). + +# 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 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 🤗 diff --git a/model_cards/dbmdz/bert-base-italian-cased/README.md b/model_cards/dbmdz/bert-base-italian-cased/README.md new file mode 100644 index 0000000000..549c1133af --- /dev/null +++ b/model_cards/dbmdz/bert-base-italian-cased/README.md @@ -0,0 +1,73 @@ +# 🤗 + 📚 dbmdz BERT models + +In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State +Library open sources Italian BERT 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. + +## 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) + +## Results + +For results on downstream tasks like NER or PoS tagging, please refer to +[this repository](https://github.com/stefan-it/fine-tuned-berts-seq). + +## Usage + +With Transformers >= 2.3 our Italian BERT models can be loaded like: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-cased") +``` + +To load the (recommended) Italian XXL BERT models, just use: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +``` + +# 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 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 🤗 diff --git a/model_cards/dbmdz/bert-base-italian-uncased/README.md b/model_cards/dbmdz/bert-base-italian-uncased/README.md new file mode 100644 index 0000000000..549c1133af --- /dev/null +++ b/model_cards/dbmdz/bert-base-italian-uncased/README.md @@ -0,0 +1,73 @@ +# 🤗 + 📚 dbmdz BERT models + +In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State +Library open sources Italian BERT 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. + +## 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) + +## Results + +For results on downstream tasks like NER or PoS tagging, please refer to +[this repository](https://github.com/stefan-it/fine-tuned-berts-seq). + +## Usage + +With Transformers >= 2.3 our Italian BERT models can be loaded like: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-cased") +``` + +To load the (recommended) Italian XXL BERT models, just use: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +``` + +# 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 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 🤗 diff --git a/model_cards/dbmdz/bert-base-italian-xxl-cased/README.md b/model_cards/dbmdz/bert-base-italian-xxl-cased/README.md new file mode 100644 index 0000000000..549c1133af --- /dev/null +++ b/model_cards/dbmdz/bert-base-italian-xxl-cased/README.md @@ -0,0 +1,73 @@ +# 🤗 + 📚 dbmdz BERT models + +In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State +Library open sources Italian BERT 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. + +## 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) + +## Results + +For results on downstream tasks like NER or PoS tagging, please refer to +[this repository](https://github.com/stefan-it/fine-tuned-berts-seq). + +## Usage + +With Transformers >= 2.3 our Italian BERT models can be loaded like: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-cased") +``` + +To load the (recommended) Italian XXL BERT models, just use: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +``` + +# 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 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 🤗 diff --git a/model_cards/dbmdz/bert-base-italian-xxl-uncased/README.md b/model_cards/dbmdz/bert-base-italian-xxl-uncased/README.md new file mode 100644 index 0000000000..549c1133af --- /dev/null +++ b/model_cards/dbmdz/bert-base-italian-xxl-uncased/README.md @@ -0,0 +1,73 @@ +# 🤗 + 📚 dbmdz BERT models + +In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State +Library open sources Italian BERT 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. + +## 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) + +## Results + +For results on downstream tasks like NER or PoS tagging, please refer to +[this repository](https://github.com/stefan-it/fine-tuned-berts-seq). + +## Usage + +With Transformers >= 2.3 our Italian BERT models can be loaded like: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-cased") +``` + +To load the (recommended) Italian XXL BERT models, just use: + +```python +from transformers import AutoModel, AutoTokenizer + +tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +model = AutoModel.from_pretrained("dbmdz/bert-base-italian-xxl-cased") +``` + +# 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 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 🤗