Text Classification
Transformers
TensorFlow
ONNX
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use tarekziade/topic_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarekziade/topic_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tarekziade/topic_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tarekziade/topic_classification") model = AutoModelForSequenceClassification.from_pretrained("tarekziade/topic_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from tarekziade/topic_classification: direct link, hf CLI and curl.
- Browser
- Download file 13.3 MB
-
https://huggingface.co/tarekziade/topic_classification/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://tarekziade/topic_classification/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/tarekziade/topic_classification/resolve/main/onnx/model_quantized.onnx
13.3 MB
- Xet hash:
- d4c6b4b36d5d5feedccd6417b4222d12122921ee5414d9c3cab72ff49f86239b
- Size of remote file:
- 13.3 MB
- SHA256:
- db08136f6794c723d017b22237cba58a2185fabb09d48e1710613850bedf08bd
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