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")# pip install -U transformers accelerate # 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
Download tokenizer.json from Dauka-transformers/interpro_bert_2: direct link, hf CLI and curl.
- Browser
- Download file 16.2 MB
-
https://huggingface.co/Dauka-transformers/interpro_bert_2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Dauka-transformers/interpro_bert_2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Dauka-transformers/interpro_bert_2/resolve/main/tokenizer.json
16.2 MB
- Xet hash:
- da5f078f69bdf6696ebae6e6de1178c8f0291c9a32b2860c2f67448c5afc4fd4
- Size of remote file:
- 16.2 MB
- SHA256:
- 228af9df9af4161ad4520213548c2d26ac237b2d24af9517dda7e005a1e96fa9
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