Instructions to use SebastianKotstein/restberta-qa-parameter-matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SebastianKotstein/restberta-qa-parameter-matching with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="SebastianKotstein/restberta-qa-parameter-matching")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SebastianKotstein/restberta-qa-parameter-matching") model = AutoModelForQuestionAnswering.from_pretrained("SebastianKotstein/restberta-qa-parameter-matching", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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RESTBERTa requires a special output interpreter that processes the predictions made by the model in order to determine the suggested parameter. We discuss the details in the paper.
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# Hyperparameters:
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The model was fine-tuned with ten epochs and a batch size of 16 on
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# References:
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- https://github.com/SebastianKotstein/Parameter-Matching-Web-APIs
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RESTBERTa requires a special output interpreter that processes the predictions made by the model in order to determine the suggested parameter. We discuss the details in the paper.
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# Hyperparameters:
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The model was fine-tuned with ten epochs and a batch size of 16 on an Nvidia RTX 3090 GPU with 24 GB. This repository contains the model checkpoint (weights) after five epochs of fine-tuning, which achieved the highest accuracy applied to our validation set.
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# References:
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- https://github.com/SebastianKotstein/Parameter-Matching-Web-APIs
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