Feature Extraction
Transformers
PyTorch
Safetensors
English
t5
text2text-generation
protein language model
computational-biology
high-performance-computing
machine-learning
language-modeling
deep-learning
proteins
chemistry
natural-language-processing
rostlab
virtual-human-chc
Instructions to use virtual-human-chc/prot_t5_xl_uniref50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use virtual-human-chc/prot_t5_xl_uniref50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="virtual-human-chc/prot_t5_xl_uniref50")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("virtual-human-chc/prot_t5_xl_uniref50") model = AutoModelForSeq2SeqLM.from_pretrained("virtual-human-chc/prot_t5_xl_uniref50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 474e773b4b8e28d0c9c92d51ca2d3a810740f48e6ef3da74c56e5f4fce2edf49
- Size of remote file:
- 11.3 GB
- SHA256:
- c06141d42e93c12b5f6d95c701952156bd4079661f5f2d981d0d2036ba96dae9
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