Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
depression
BERT
mental-health
Eval Results (legacy)
text-embeddings-inference
Instructions to use poudel/Depression_and_Non-Depression_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use poudel/Depression_and_Non-Depression_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="poudel/Depression_and_Non-Depression_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("poudel/Depression_and_Non-Depression_Classifier") model = AutoModelForSequenceClassification.from_pretrained("poudel/Depression_and_Non-Depression_Classifier") - Notebooks
- Google Colab
- Kaggle
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language:
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- en
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base_model: google-bert/bert-base-uncased
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# Model Card for Sentiment Classifier for Depression
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language:
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- en
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base_model: google-bert/bert-base-uncased
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metrics:
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- Accuracy
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- Recall
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- Percision
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# Model Card for Sentiment Classifier for Depression
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