Text Classification
Transformers
PyTorch
Safetensors
French
English
multilingual
distilbert
ticket-classification
customer-support
call-center
Eval Results (legacy)
text-embeddings-inference
Instructions to use Kahouli/callcenter-ticket-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kahouli/callcenter-ticket-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kahouli/callcenter-ticket-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kahouli/callcenter-ticket-classifier") model = AutoModelForSequenceClassification.from_pretrained("Kahouli/callcenter-ticket-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9be3d393805cc210232de0a83e29c6d8cd918bb6bae480edc5d0d89cd97ad7a0
- Size of remote file:
- 541 MB
- SHA256:
- d047e2afd8b0fba80510b702006df462b61ad31029aef97f2be969e58ce664f9
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