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
File size: 713 Bytes
ea6e063 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"per_channel": true,
"reduce_range": true,
"per_model_config": {
"model": {
"op_types": [
"Add",
"Gather",
"GlobalAveragePool",
"Tanh",
"MatMul",
"Reshape",
"Softmax",
"Slice",
"Reciprocal",
"Cast",
"Shape",
"Concat",
"Squeeze",
"Mul",
"Unsqueeze",
"Erf",
"Range",
"Transpose",
"Sqrt",
"Sub"
],
"weight_type": "QInt8"
}
}
} |