| 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). | |
| 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. | |
| ## Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModel | |
| model_name = "brjoey/climatebert_econbert" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # Load the base model | |
| model = AutoModel.from_pretrained(model_name, torch_dtype="auto") | |
| # For sequence classification tasks | |
| from transformers import AutoModelForSequenceClassification | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3) | |
| ``` | |
| I do not recommend fine-tuning this model for sentiment 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:\ | |
| https://huggingface.co/brjoey/CBSI-bert-large-uncased \ | |
| https://huggingface.co/brjoey/CBSI-bert-base-uncased |