Instructions to use krotima1/AlignScoreCS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krotima1/AlignScoreCS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="krotima1/AlignScoreCS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("krotima1/AlignScoreCS") model = AutoModelForSequenceClassification.from_pretrained("krotima1/AlignScoreCS") - Notebooks
- Google Colab
- Kaggle
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README.md
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# AlignScoreCS
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MultiTask multilingual model for assessing facticity in various NLU tasks in Czech and English language. We followed the initial paper AlignScore https://arxiv.org/abs/2305.16739.
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We trained a model of checkpoint xlm-roberta-large https://huggingface.co/FacebookAI/xlm-roberta-large with three linear layers for regression,
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# Usage
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# AlignScoreCS
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MultiTask multilingual model for assessing facticity in various NLU tasks in Czech and English language. We followed the initial paper AlignScore https://arxiv.org/abs/2305.16739.
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We trained a model using a shared architecture of checkpoint xlm-roberta-large https://huggingface.co/FacebookAI/xlm-roberta-large with three linear layers for regression,
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binary classification and ternary classification.
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# Usage
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