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Explore AI models 27
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources French Europeana BERT models 🎉 We extracted all French texts using the `language` metadata attribute from the Europeana corpus. Based on the metadata information, texts from the 18th - 20th century are mainly included in the training corpus.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources another German BERT models 🎉 In addition to the recently released German BERT model by deepset 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.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources another German BERT models 🎉 In addition to the recently released German BERT model by deepset 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.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources Italian BERT and ELECTRA models 🎉 The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the OPUS corpora 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.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources Italian BERT and ELECTRA models 🎉 The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the OPUS corpora 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.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources Italian BERT and ELECTRA models 🎉 The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the OPUS corpora 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.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources an uncased model for Turkish 🎉 Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the model name: BERTurk. The current version of the model is trained on a filtered and sentence segmented version of the Turkish OSCAR corpus, a recent Wikipedia dump, various OPUS corpora and a special corpus provided by Kemal Oflazer.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased model for Turkish 🎉 Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the model name: BERTurk. The current version of the model is trained on a filtered and sentence segmented version of the Turkish OSCAR corpus, a recent Wikipedia dump, various OPUS corpora and a special corpus provided by Kemal Oflazer.
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View details ↗In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources an uncased model for Turkish 🎉 Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the model name: BERTurk. The current version of the model is trained on a filtered and sentence segmented version of the Turkish OSCAR corpus, a recent Wikipedia dump, various OPUS corpora and a special corpus provided by Kemal Oflazer.
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View details ↗Our Historic Language Models Zoo contains support for the following languages - incl. their training data source: **Notice**: We have released language models for Historic German and French trained on more noisier data earlier - see this repo for more information: We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size and use less-noisier data:
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