Feature Extraction
Transformers
TensorBoard
Safetensors
bert
fill-mask
trained_from_scratch
text-embeddings-inference
Instructions to use Dauka-transformers/interpro_bert_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dauka-transformers/interpro_bert_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Dauka-transformers/interpro_bert_2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Dauka-transformers/interpro_bert_2") model = AutoModelForMaskedLM.from_pretrained("Dauka-transformers/interpro_bert_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bb2ef0f459b3d9a992bd57e99ade834ac5e9d68320365479a59745cd669fb26
- Size of remote file:
- 4.92 kB
- SHA256:
- 44afdbc5df6b0cb53559d7fea40b29f4c6dc7ef2818525ef43d615cd2b816b18
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