Instructions to use Synthyra/ANKH_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ANKH_base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Synthyra/ANKH_base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 821 Bytes
4b8f0f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
Encoder only version of the [ANKH base model](https://huggingface.co/ElnaggarLab/ankh-base) ([paper](https://arxiv.org/abs/2301.06568)). The encoder only version is ideal for protein representation tasks.
## To download
```python
from transformers import T5EncoderModel, AutoTokenizer
model_path = 'Synthyra/ANKH_base'
model = T5EncoderModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
```
We are working on implementing a version of T5 based PLMs with [Flex attention](https://pytorch.org/blog/flexattention/) once learned relative position bias is supported (used in T5). Stay tuned. |