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deborahcodes
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DistilBERT-depression-detector

Text Classification
Transformers
Safetensors
distilbert
text-embeddings-inference
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How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="deborahcodes/DistilBERT-depression-detector")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("deborahcodes/DistilBERT-depression-detector")
model = AutoModelForSequenceClassification.from_pretrained("deborahcodes/DistilBERT-depression-detector")
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Depression Detector

Helps in early detection of depression based on sentiment analysis

  • Developed by: deborahcodes
  • Finetuned from model: distilbert-base-uncased
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Model size
67M params
Tensor type
F32
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Dataset used to train deborahcodes/DistilBERT-depression-detector