Instructions to use teppei727/bert-large-relation14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teppei727/bert-large-relation14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="teppei727/bert-large-relation14")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("teppei727/bert-large-relation14") model = AutoModelForSequenceClassification.from_pretrained("teppei727/bert-large-relation14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Finetuned BERT model for 14-class classification. It was introduced in the paper: [Automatic Slide Generation Using Discourse Relations](https://link.springer.com/chapter/10.1007/978-3-031-36336-8_61) and first released in this repository. This model is uncased: it does not make a difference between english and English.
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In our proposed method in this [paper](https://link.springer.com/chapter/10.1007/978-3-031-36336-8_61), we only used this model for the classification of discourse relation between the FIRST and SECOND sentence in summarized sentences. The model that is used between the other sentences is [this model](https://huggingface.co/
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# Descliption
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Finetuned BERT model for 14-class classification. It was introduced in the paper: [Automatic Slide Generation Using Discourse Relations](https://link.springer.com/chapter/10.1007/978-3-031-36336-8_61) and first released in this repository. This model is uncased: it does not make a difference between english and English.
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In our proposed method in this [paper](https://link.springer.com/chapter/10.1007/978-3-031-36336-8_61), we only used this model for the classification of discourse relation between the FIRST and SECOND sentence in summarized sentences. The model that is used between the other sentences is [this model](https://huggingface.co/bert_woco). If you are curious about our proposed method, it's better to see that model.
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# Descliption
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