Instructions to use ThomasKaspereit/IAS40BERT-sentences with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasKaspereit/IAS40BERT-sentences with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ThomasKaspereit/IAS40BERT-sentences")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ThomasKaspereit/IAS40BERT-sentences") model = AutoModelForSequenceClassification.from_pretrained("ThomasKaspereit/IAS40BERT-sentences") - Notebooks
- Google Colab
- Kaggle
This model estimates the likelihood that a sentence from an earnings call is related to (re)valuation. In this context, “revaluation” refers to changes in the fair value of investment property under IAS 40 (Investment Property).
The model is described and applied in the article “Don’t you know? They are talking about a revaluation—Market reactions to gains or losses on investment property and earnings call sentiment” (link to be added after publication).
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Model tree for ThomasKaspereit/IAS40BERT-sentences
Base model
google-bert/bert-base-uncased