Instructions to use SebastianKotstein/restberta-qa-endpoint-discovery with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SebastianKotstein/restberta-qa-endpoint-discovery 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-endpoint-discovery")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SebastianKotstein/restberta-qa-endpoint-discovery") model = AutoModelForQuestionAnswering.from_pretrained("SebastianKotstein/restberta-qa-endpoint-discovery", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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a model for a specific Web API integration task.
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# RESTBERTa for Endpoint Discovery:
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This repository contains the weights of a fined-tuned
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Given a query in natural language that describes the purpose and behavior of the target endpoint, i.e., its semantics, the model should choose the endpoint from
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a given URI model, which is a tree structure.
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a model for a specific Web API integration task.
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# RESTBERTa for Endpoint Discovery:
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This repository contains the weights of a fined-tuned CodeBERT base model for the task of endpoint discovery in Web APIs. For this, we formulate question answering as a multiple choice task:
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Given a query in natural language that describes the purpose and behavior of the target endpoint, i.e., its semantics, the model should choose the endpoint from
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a given URI model, which is a tree structure.
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