Upload model.py

model.py

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+import transformers
+from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup
+import torch
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib import rc
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+from collections import defaultdict
+from textwrap import wrap
+
+from torch import nn, optim
+from torch.utils.data import Dataset, DataLoader
+import torch.nn.functional as F
+
+class DepressionClassifier(nn.Module):
+
+  def __init__(self, n_classes, pre_trained_model_name):
+    super(DepressionClassifier, self).__init__()
+    self.bert = BertModel.from_pretrained(pre_trained_model_name)
+    self.drop = nn.Dropout(p=0.3)
+    self.out = nn.Linear(self.bert.config.hidden_size, n_classes)
+
+  def forward(self, input_ids, attention_mask):
+    _, pooled_output = self.bert(
+      input_ids=input_ids,
+      attention_mask=attention_mask,
+      return_dict = False #here
+    )
+    output = self.drop(pooled_output)
+    return self.out(output)