Added inference.py and model weights

inference.py

@@ -0,0 +1,66 @@
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from transformers import DistilBertTokenizerFast, DistilBertModel
+import numpy as np
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
+
+class DistilBERTSent(nn.Module):
+    """
+    DistilBERT but with a layer attached to perform binary classification.
+    """
+    def __init__(self, freeze_bert=False):
+        super(DistilBERTSent, self).__init__()
+        self.distil_bert = DistilBertModel.from_pretrained('distilbert-base-uncased')
+        self.linear = nn.Linear(2304, 1)
+        self.sigmoid = nn.Sigmoid()
+        
+        if freeze_bert:
+            for param in self.distil_bert.parameters():
+                param.requires_grad = False
+                
+    def forward(self, ids, mask):
+        outputs = self.distil_bert(input_ids = ids, attention_mask=mask, output_hidden_states=True)
+        x = torch.concat(outputs.hidden_states[:-4], dim=2).mean(1)
+        x = self.linear(x)
+        x = self.sigmoid(x)
+        return x.flatten()
+    
+def initialize(path="models/model.pt"):
+    model = DistilBERTSent()
+    model.load_state_dict(torch.load(path, map_location=device))
+    model.eval()
+    return model
+
+def chunks(lst, n): # chunk list of strings
+    for i in tqdm(range(0, len(lst), n)):
+        yield lst[i:i+n]
+
+@torch.no_grad()
+def inference(model, text, batch_size=32):
+    """
+    pass in model, list of text, and batch_size 
+    """
+    to_return = []
+    for batch in chunks(text, batch_size):
+        encoded = tokenizer(
+            text = batch,
+            add_special_tokens=True,
+            padding='max_length',
+            return_attention_mask=True,
+            truncation=True
+        )
+        input_ids = torch.tensor(encoded.get('input_ids')).to(device)
+        attention_masks = torch.tensor(encoded.get('attention_mask')).to(device)
+        to_return.append(model(input_ids, attention_masks))
+
+    return torch.concat(to_return).cpu().numpy()
+
+if __name__ == "__main__":
+    model = initialize()
+    text = ["I love it so much!", "Broke on the first day"]
+    print(inference(model, text, 2))
+    

models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd3e40a660ec86d5f1b746490852d456b68a57f664bceba3a994f6704db20143
+size 265494629