Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
multi-task-learning
call-center-analytics
child-helplines
case-classification
crisis-support
social-impact
east-africa
openchlsystem
helpline
Eval Results (legacy)
text-embeddings-inference
Instructions to use openchs/cls-gbv-distilbert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openchs/cls-gbv-distilbert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openchs/cls-gbv-distilbert-v1")# Load model directly from transformers import AutoTokenizer, MultiTaskDistilBert tokenizer = AutoTokenizer.from_pretrained("openchs/cls-gbv-distilbert-v1") model = MultiTaskDistilBert.from_pretrained("openchs/cls-gbv-distilbert-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e6cc2d8728db912da86289955fb9eb62457c1277ed19c3174c198b3b39cbaee
- Size of remote file:
- 268 MB
- SHA256:
- dfc065fb48eee0664be9cd861a9bf4d4f896c73e0438bc24f6fcb087f25b4656
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.