Text Classification
Transformers
PyTorch
roberta
question_answering
qa
answer_consolidation
text-embeddings-inference
Instructions to use Salesforce/qa_consolidation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/qa_consolidation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Salesforce/qa_consolidation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Salesforce/qa_consolidation") model = AutoModelForSequenceClassification.from_pretrained("Salesforce/qa_consolidation") - Notebooks
- Google Colab
- Kaggle
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# QA Consolidation Model
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Model card for the QA Consolidation (step 3) of the Discord Questions framework (EMNLP 2022 - Findings).
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The model is a RoBERTa-large model, finetuned on the [MOCHA dataset](https://arxiv.org/abs/2010.03636), and a 5-pt version of the [Answer Equivalence](https://arxiv.org/abs/2202.07654v1) dataset. For a (question, answer1, answer2)-tuple, the model outputs a [1-5] answer similarity score, where 5 is most similar.
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# QA Consolidation Model
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Model card for the QA Consolidation (step 3) of the Discord Questions framework (EMNLP 2022 - Findings). The model assesses the similarity between two answers (a1, a2) to a question Q. The score obtained is on a scale from 1 (most dissimilar) to 5 (most similar). See example below for formatting.
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The model is a RoBERTa-large model, finetuned on the [MOCHA dataset](https://arxiv.org/abs/2010.03636), and a 5-pt version of the [Answer Equivalence](https://arxiv.org/abs/2202.07654v1) dataset. For a (question, answer1, answer2)-tuple, the model outputs a [1-5] answer similarity score, where 5 is most similar.
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