Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dhananjay2912/deberta_aci_bench_medical_section_classifier")
model = AutoModelForSequenceClassification.from_pretrained("dhananjay2912/deberta_aci_bench_medical_section_classifier", device_map="auto")Quick Links
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.4913766384124756
f1_macro: 0.28164367547346275
f1_micro: 0.64
f1_weighted: 0.5917376665887304
precision_macro: 0.2705775014459225
precision_micro: 0.64
precision_weighted: 0.5802396761133604
recall_macro: 0.3324350649350649
recall_micro: 0.64
recall_weighted: 0.64
accuracy: 0.64
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dhananjay2912/deberta_aci_bench_medical_section_classifier")