Text Classification
Transformers
PyTorch
TensorBoard
English
distilbert
qa-metrics
call-center
multi-head
transcript-analysis
customer-service
quality-assurance
child-helplines
crisis-support
social-impact
swahili
east-africa
Eval Results (legacy)
Instructions to use openchs/qa-helpline-distilbert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openchs/qa-helpline-distilbert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openchs/qa-helpline-distilbert-v1")# Load model directly from transformers import AutoTokenizer, MultiHeadQAClassifier tokenizer = AutoTokenizer.from_pretrained("openchs/qa-helpline-distilbert-v1") model = MultiHeadQAClassifier.from_pretrained("openchs/qa-helpline-distilbert-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openchs/qa-helpline-distilbert-v1: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/openchs/qa-helpline-distilbert-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://openchs/qa-helpline-distilbert-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openchs/qa-helpline-distilbert-v1/resolve/main/pytorch_model.bin
266 MB
- Xet hash:
- 78e52a9e121a51a3fac798514258394f8bf1d48528a2c0b80096effcd7aa27dd
- Size of remote file:
- 266 MB
- SHA256:
- 49cd26533720192c40599d70027931f9439481e44d1fe80a35e77509564bf77e
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