Model reference · open weights
convbert-turkish-cased is an open-weight embedding model from dbmdz. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | dbmdz |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 107M |
| Context | 512 tokens |
| Runs with | transformers |
| Released | 2022-03-02 |
| Popularity | 507 downloads / month |
| Licence | Open weights |
About
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ConvBERT model for Turkish 🎉
ConvBERTurk is a community-driven cased ConvBERT model for Turkish.
In addition to the BERT and ELECTRA based models, we also trained a ConvBERT model. The ConvBERT architecture is presented in the "ConvBERT: Improving BERT with Span-based Dynamic Convolution" paper.
We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-32 TPU.
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.
The final training corpus has a size of 35GB and 44,04,976,662 tokens.
Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model on a TPU v3-32!
With Transformers >= 4.3 our cased ConvBERT model can be loaded like:
from transformers import AutoModel, AutoTokenizer
model_name = "dbmdz/convbert-base-turkish-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
For results on PoS tagging, NER and Question Answering downstream tasks, please refer to this repository.
All models are available on the Huggingface model hub.
For questions about our DBMDZ BERT models in general, just open an issue here 🤗
Thanks to Kemal Oflazer for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation.
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 team, it is possible to download both cased and uncased models from their S3 storage 🤗
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys convbert-turkish-cased for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (convbert-turkish-cased below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"convbert-turkish-cased","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.