Upload model.py

model.py

@@ -0,0 +1,38 @@
+import streamlit as st
+from torch import nn
+import numpy as np
+from transformers import DistilBertForSequenceClassification
+
+
+class ArxivModel:
+
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
+
+        self.model.to('cpu')
+
+    def get_logits(self, tweet_text):
+        text_tokens = self.tokenizer(tweet_text, return_tensors="pt").to('cpu')
+        softmax = nn.Softmax(dim=1)
+
+        return softmax(self.model(**text_tokens).logits.detach()).numpy()[0]
+
+    def get_idx_class(self, tweet_text, thr=-1.0):
+        logits = self.get_logits(tweet_text)
+
+        if thr == -1.0:
+            return [(np.argmax(logits), np.max(logits))]
+        else:
+            sum_probs = 0.0
+            idxs = []
+            for p in np.argsort(logits)[::-1]:
+                sum_probs += logits[p]
+                idxs.append((p, logits[p]))
+                if sum_probs > thr:
+                    return idxs
+
+
+@st.cache
+def load_model(path="./checkpoint-15500", num_labels=153):
+    return DistilBertForSequenceClassification.from_pretrained(path, num_labels=num_labels)