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
Download training_args.bin from Kahouli/callcenter-ticket-classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/Kahouli/callcenter-ticket-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://Kahouli/callcenter-ticket-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Kahouli/callcenter-ticket-classifier/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 4782f3f1ab4ce4796d8f9a81372b1026efa372120d85c034b163556e571812c4
- Size of remote file:
- 5.91 kB
- SHA256:
- 01868e94c7ee898cd33e9096755e10e1a849879be6f718356948e5ae9106823b
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