Question Answering
Transformers
PyTorch
TensorBoard
English
bert
multiple-choice
Generated from Trainer
Multiple Choice
Instructions to use DunnBC22/bert-base-uncased-Figurative_Language with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/bert-base-uncased-Figurative_Language with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="DunnBC22/bert-base-uncased-Figurative_Language")# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("DunnBC22/bert-base-uncased-Figurative_Language") model = AutoModelForMultipleChoice.from_pretrained("DunnBC22/bert-base-uncased-Figurative_Language") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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- Multiple Choice
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metrics:
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- accuracy
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model-index:
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- name: bert-base-uncased-Figurative_Language
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results: []
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language:
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- en
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pipeline_tag: question-answering
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---
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# bert-base-uncased-Figurative_Language
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---
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language:
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- en
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license: apache-2.0
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tags:
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- generated_from_trainer
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- Multiple Choice
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metrics:
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- accuracy
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pipeline_tag: question-answering
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base_model: bert-base-uncased
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model-index:
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- name: bert-base-uncased-Figurative_Language
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results: []
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---
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# bert-base-uncased-Figurative_Language
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