Instructions to use Sarmila/distilbert-base-uncased-distilled-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sarmila/distilbert-base-uncased-distilled-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Sarmila/distilbert-base-uncased-distilled-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Sarmila/distilbert-base-uncased-distilled-squad") model = AutoModelForQuestionAnswering.from_pretrained("Sarmila/distilbert-base-uncased-distilled-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Model description
PyTorch implementation containing all the modelling needed for your NLP task. Combines a language
model and a prediction head. Allows for gradient flow back to the language model component.
Model Type
not defined
Model Details
- version: 1
- device: cuda
- number of labels: not found
- number_of_parameters: 66364418
- base_model: this is a base model itself
Training
No Information
Evaluation
No Information
quantitative_analyses
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ethical_considerations
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caveats_and_recommendations
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