Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use jonaskoenig/topic_classification_03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonaskoenig/topic_classification_03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonaskoenig/topic_classification_03")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonaskoenig/topic_classification_03") model = AutoModelForSequenceClassification.from_pretrained("jonaskoenig/topic_classification_03") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6dff4e6fd33d9d8a53358a6e3d8c6b9a8124f6b4557188b60da4289c9213917
- Size of remote file:
- 51.2 MB
- SHA256:
- fd6cf4d9fb2c38dccf4e8e17ee1e7ccedf37258eb0ff0b4eaf65e23055a56b80
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.