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:
- 84dffd14dbbd60a7e2b52f553c47cfc9e432fdca0714591a823bea8009b58895
- Size of remote file:
- 5.71 kB
- SHA256:
- cf30f943a10d8306a322e67590c66171ef7c118ca7717a00e977be6172ec3504
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