Text Classification
Transformers
PyTorch
English
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use andi611/distilbert-base-uncased-qa-boolq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andi611/distilbert-base-uncased-qa-boolq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="andi611/distilbert-base-uncased-qa-boolq")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("andi611/distilbert-base-uncased-qa-boolq") model = AutoModelForSequenceClassification.from_pretrained("andi611/distilbert-base-uncased-qa-boolq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cc58c74640274776e931d787fc19dd5d3b197d9eeaef99f4ed61cfd6354a852
- Size of remote file:
- 268 MB
- SHA256:
- 364d06f2321d04dbdac461a7b7affdf3c81adb8c2864b7c2a8d663843598e0c4
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