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---
license: apache-2.0
---
### DreamBank Custom Architecture
The repo contains the weights for the custom architecture presented in the paper [Automatic Annotation of Dream Report’s Emotional Content with Large Language Models](https://aclanthology.org/2024.clpsych-1.7/).
A working example of how to load and use the model can be found below. Please refer to the [Git repo](https://github.com/lorenzoscottb/Dream_Reports_Annotation/tree/main/Experiments/Supervised_Learning) for more details.
#### Use
```py
import torch, os
import pandas as pd
from tqdm import tqdm
import transformers
from transformers import AutoModel
from transformers import AutoConfig
from transformers import BertTokenizerFast
from SL_utils import *
Coding_emotions = {
"AN": "Anger",
"AP": "Apprehension",
"SD": "Sadness",
"CO": "Confusion",
"HA": "Happiness",
}
emotions_list = list(Coding_emotions.keys())
test_sentences = [
"In my dream I was follwed by the scary monster.",
"I was walking in a forest, sorrounded by singing birds. I was in calm and peace."
]
test_sentences_target = len(test_sentences)*[[0, 0, 0, 0, 0]]
test_sentences_df = pd.DataFrame.from_dict(
{
"report":test_sentences,
"Report_as_Multilabel":test_sentences_target
}
)
```
```py
model_name = "bert-large-cased"
model_config = AutoConfig.from_pretrained(model_name)
tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=False)
testing_set = CustomDataset(test_sentences_df, tokenizer, max_length=512)
test_params = {
'batch_size': 2,
'shuffle': True,
'num_workers': 0
}
testing_loader = DataLoader(testing_set, **test_params)
model = BERT_PTM(
model_config,
model_name=model_name,
n_classes=len(emotions_list),
freeze_BERT=False,
)
# Load the models' weights from the pre-treined model
model.load_state_dict(torch.load("path/to/pytorch_model.bin"))
model.to("cuda")
```
```py
outputs, targets, ids = validation(model, testing_loader, device="cuda", return_inputs=True)
corr_outputs = np.array(outputs) >= 0.5
corr_outputs_df = pd.DataFrame(corr_outputs, columns=emotions_list)
corr_outputs_df = corr_outputs_df.astype(int)
corr_outputs_df["report"] = decoded_ids = [decode_clean(x, tokenizer) for x in tqdm(ids)]
```
### Cite
If you use this model on your work or research, please cite as:
```bibtex
@inproceedings{bertolini-etal-2024-automatic,
title = "Automatic Annotation of Dream Report{'}s Emotional Content with Large Language Models",
author = "Bertolini, Lorenzo and
Elce, Valentina and
Michalak, Adriana and
Widhoelzl, Hanna-Sophia and
Bernardi, Giulio and
Weeds, Julie",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.7",
pages = "92--107",
}
``` |