Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use jonaskoenig/xtremedistil-l6-h256-uncased-question-vs-statement-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonaskoenig/xtremedistil-l6-h256-uncased-question-vs-statement-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonaskoenig/xtremedistil-l6-h256-uncased-question-vs-statement-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonaskoenig/xtremedistil-l6-h256-uncased-question-vs-statement-classifier") model = AutoModelForSequenceClassification.from_pretrained("jonaskoenig/xtremedistil-l6-h256-uncased-question-vs-statement-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e801be8e3a5f75a85d50ec90e562e19c25b807068c303366d64f4e6ed5a95965
- Size of remote file:
- 51.2 MB
- SHA256:
- 7e1053c5e8b9084611af6d1936fa2011bdfbf84aab09611fff0fc108ae55bcce
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