Text Classification
Transformers
Safetensors
English
bert
medical
icd10
multilabel-classification
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sshan95/medicoder-ai-v2-model")
model = AutoModelForSequenceClassification.from_pretrained("sshan95/medicoder-ai-v2-model")Quick Links
MediCoder AI v2 - Fixed Model
This is a properly formatted version of the MediCoder model for medical code classification.
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sshan95/medicoder-ai-v2-model")
model = AutoModelForSequenceClassification.from_pretrained("sshan95/medicoder-ai-v2-model")
Configuration
- Labels: 25,719 ICD-10 codes
- Architecture: BERT-based
- Task: Multi-label medical code classification
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sshan95/medicoder-ai-v2-model")