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--- |
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language: |
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- en |
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base_model: |
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- climatebert/econbert |
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library_name: transformers |
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--- |
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This repository contains a version of [climatebert/econbert](https://huggingface.co/climatebert/econbert) with a corrected folder structure, ensuring compatibility with standard Hugging Face Transformers methods (AutoTokenizer, AutoModel, AutoModelForSequenceClassification). |
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For more information about the original model, please refer to the official repository [climatebert/econbert](https://huggingface.co/climatebert/econbert), and cite the corresponding [paper](https://dx.doi.org/10.2139/ssrn.5263616) if you use this model. |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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model_name = "brjoey/climatebert_econbert" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Load the base model |
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model = AutoModel.from_pretrained(model_name, torch_dtype="auto") |
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# For sequence classification tasks |
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from transformers import AutoModelForSequenceClassification |
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model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3) |
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``` |
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I do not recommend fine-tuning this model for monetary policy stance classification on the replication data of [Nițoi et al. (2023)](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/40JFEK) as its performance is not competitive with fine-tuned BERT-base models. For this task, fine-tuned BERT-based models, as proposed by Nițoi et al. (2023), are available here:\ |
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https://huggingface.co/brjoey/CBSI-bert-large-uncased \ |
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https://huggingface.co/brjoey/CBSI-bert-base-uncased |
