Text Classification
Transformers
Safetensors
bert
mental-health
multi-label
mental-bert-base-uncased
text-embeddings-inference
Instructions to use mahir2021/mental-bert-depEMO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahir2021/mental-bert-depEMO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mahir2021/mental-bert-depEMO")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mahir2021/mental-bert-depEMO") model = AutoModelForSequenceClassification.from_pretrained("mahir2021/mental-bert-depEMO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Mental-BERT
Multi-label emotion classifier trained on depression-related text.
- Base model:
mental/mental-bert-base-uncased - Architecture: Standard AutoModelForSequenceClassification
- Labels (8): anger, brain dysfunction (forget), emptiness, hopelessness, loneliness, sadness, suicide intent, worthlessness
- Seed: 42, Epochs: 5, LR: 2e-05, MaxLen: 256
Test metrics
{
"eval_loss": 0.7333596348762512,
"eval_P-Mic": 0.8343140291111799,
"eval_R-Mic": 0.8053811659192825,
"eval_F1-Mic": 0.8195923334347429,
"eval_P-Mac": 0.807471674923893,
"eval_R-Mac": 0.7395350139624512,
"eval_F1-Mac": 0.7559934890149667
}
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