bert-base-greek-uncased-v1

Model overview

* The Greek part of Wikipedia, * The Greek part of European Parliament Proceedings Parallel Corpus, and * The Greek part of OSCAR, a cleansed version of Common Crawl. * The entire corpus of Greek legislation, as published by the National Publication Office, * The entire corpus of EU legislation (Greek translation), as published in Eur-Lex. * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert).* We then used Hugging Face's Transformers conversion script to convert the TF checkpoint and vocabulary in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users. * We released a model similar to the English `bert-base-uncased` model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from TensorFlow Research Cloud (TFRC), while also utilizing GCP research credits. Huge thanks to both Google programs for supporting us!

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：来源未披露，须进一步核验。模型介绍与第三方评测均以原来源的具体版本和方法为准。