Text Classification
Transformers
Safetensors
bert
proteins
Biology
classifier
text-embeddings-inference
Instructions to use oohtmeel/Bert_protein_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oohtmeel/Bert_protein_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oohtmeel/Bert_protein_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oohtmeel/Bert_protein_classifier") model = AutoModelForSequenceClassification.from_pretrained("oohtmeel/Bert_protein_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c2a18472f29e388b4e7bfa17f109d077dc549dc6ddfe466c01a065d6e8300cf
- Size of remote file:
- 438 MB
- SHA256:
- 1576fd7458ec527084a815495862fe165368ff84b81b32e6da2be93a41fae1af
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