mahir2021/mental-bert-depemo2

Mental-BERT

Multi-label emotion classifier trained on the depEMO dataset.

• Base encoder: mental/mental-bert-base-uncased
• Architecture: CLSDiscriminator (encoder → CLS → head)
• Labels (8): anger, brain dysfunction (forget), emptiness, hopelessness, loneliness, sadness, suicide intent, worthlessness
• Best seed: 43, Epochs: 5, LR: 2e-05, MaxLen: 192
• Per-class threshold tuning on the validation set was applied.

Test metrics (seed-averaged predictions)

• eval_P-Mic: 0.7567
• eval_R-Mic: 0.8927
• eval_F1-Mic: 0.8191
• eval_P-Mac: 0.7294
• eval_R-Mac: 0.8595
• eval_F1-Mac: 0.7870
• eval_loss: 0.3720

Files

• config.json, model.safetensors, tokenizer*: HuggingFace encoder
• custom_head.bin: torch.state_dict of the classification head (plus adv head and/or BiLSTM if applicable)
• custom_config.json: metadata to rebuild the head
• emotion_mapping.json: label ↔ index
• hyperparameters.json, split_indices.json, training_log.json
• per_label_report.csv, test_results.json

Reload

from transformers import AutoModel, AutoTokenizer
import torch, json
from huggingface_hub import hf_hub_download

repo_id = "mahir2021/mental-bert-depemo2"
cfg  = json.load(open(hf_hub_download(repo_id, 'custom_config.json')))
enc  = AutoModel.from_pretrained(repo_id)
tok  = AutoTokenizer.from_pretrained(repo_id)
head = torch.load(hf_hub_download(repo_id, 'custom_head.bin'), map_location='cpu')
# then instantiate the head class from cfg['arch'] and load_state_dict(head)