Text Classification
Transformers
Safetensors
English
distilbert
mental-health
transformer
depression
anxiety
clinical-nlp
huggingface
Eval Results (legacy)
text-embeddings-inference
Instructions to use dsuram/distilbert-mentalhealth-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsuram/distilbert-mentalhealth-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dsuram/distilbert-mentalhealth-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dsuram/distilbert-mentalhealth-classifier") model = AutoModelForSequenceClassification.from_pretrained("dsuram/distilbert-mentalhealth-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34ddae79d99b47ead736aee0d18dd1b71b677d0f15174f0b606e00e2e67ff105
- Size of remote file:
- 268 MB
- SHA256:
- ebb65ab8469503d6342322f780be808f6f588744e8fce001849ae4bbc584704a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.