Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use reddgr/tl-test-learn-prompt-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reddgr/tl-test-learn-prompt-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="reddgr/tl-test-learn-prompt-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("reddgr/tl-test-learn-prompt-classifier") model = AutoModelForSequenceClassification.from_pretrained("reddgr/tl-test-learn-prompt-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa7124fb48cf2e46180ee4d82b976245af9510dc2cbb770eed10102fd4a4db20
- Size of remote file:
- 268 MB
- SHA256:
- 5ece8d0fa77d0496739c03d667080001e3c4396f75dd60a32f96117222307604
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.