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:
- f73aef646da90a7a4cdee51863f4f079ffcb53ce0ad6c991ebbda88b6a5b678c
- Size of remote file:
- 438 MB
- SHA256:
- 9e531df8f48e93c52dd9bdda835bfbe0e7a13866aed1aa7681d8cf7ef0033fc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.