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 quantize_config.json from tarekziade/topic_classification: direct link, hf CLI and curl.
- Browser
- Download file 713 Bytes
-
https://huggingface.co/tarekziade/topic_classification/resolve/main/quantize_config.json
- Command line
-
hf download hf://tarekziade/topic_classification/quantize_config.json
-
curl -L -o quantize_config.json https://huggingface.co/tarekziade/topic_classification/resolve/main/quantize_config.json
713 Bytes
| { | |
| "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" | |
| } | |
| } | |
| } |