DReAMy-lib/DB-custom-architecture

DreamBank Custom Architecture

The repo contains the weights for the custom architecture in Bertolini et al., 2023. Working example on how to load and use the model can be found in the Git repo.

Use

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
                }
)

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")

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 the model, please cite the pre-print.

@misc{https://doi.org/10.48550/arxiv.2302.14828,
  doi = {10.48550/ARXIV.2302.14828},
  url = {https://arxiv.org/abs/2302.14828},
  author = {Bertolini, Lorenzo and Elce, Valentina and Michalak, Adriana and Bernardi, Giulio and Weeds, Julie},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Automatic Scoring of Dream Reports' Emotional Content with Large Language Models},
  publisher = {arXiv},
  year = {2023},
  copyright = {Creative Commons Attribution 4.0 International}
}