Initial commit

app.py

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+import streamlit as st
+from model import classify_text
+
+
+st.markdown("### Sentiment classification (negative vs. positive)")
+title = st.text_area("Your sentiment for classification", "I would not use it")
+if st.button("Classify!"):
+    prob = float(classify_text(title))
+    st.markdown(f"**Model**:     {round(prob, 5)}% of being positive")

model.py

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+import os.path
+
+import torch
+import torch.nn as nn
+from transformers import RobertaTokenizerFast, RobertaForMaskedLM
+import streamlit as st
+
+
+class SimpleClassifier(nn.Module):
+    def __init__(self, in_features: int, hidden_features: int,
+                 out_features: int, activation=nn.ReLU()):
+        super().__init__()
+        self.bn = nn.BatchNorm1d(in_features)
+        self.in2hid = nn.Linear(in_features, hidden_features)
+        self.activation = activation
+        self.hid2hid = nn.Linear(hidden_features, hidden_features)
+        self.hid2out = nn.Linear(hidden_features, out_features)
+
+
+        #unused
+        self.bn2 = nn.BatchNorm1d(hidden_features)
+
+    def forward(self, X):
+        X = self.bn(X)
+        X = self.in2hid(X)
+
+        X = self.activation(X)
+        X = self.hid2hid(torch.concat((X,), 1))
+
+        X = self.activation(X)
+        X = self.hid2out(torch.concat((X,), 1))
+
+        X = nn.functional.sigmoid(X)
+        return X
+
+
+@st.cache_data()
+def load_models():
+    model = RobertaForMaskedLM.from_pretrained("roberta-base")
+    model.lm_head = nn.Identity()
+    tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
+    my_classifier = SimpleClassifier(768, 768, 1)
+    weights_path = os.path.join(__file__, "..", "twitter_model_91_5-.pth")
+    my_classifier.load_state_dict(torch.load(weights_path, map_location=device))
+    my_classifier.eval()
+    return {
+        "tokenizer": tokenizer,
+        "model": model,
+        "classifier": my_classifier
+    }
+
+
+def classify_text(text: str) -> float:
+    models = load_models()
+    tokenizer, model, classifier = models["tokenizer"], models["model"], models["classifier"]
+
+    X = tokenizer(
+        text,
+        truncation=True,
+        max_length=128,
+        return_tensors='pt'
+    )["input_ids"]
+
+    X = model.forward(X)[-1][0].sum(axis=0)[None, :]
+    return classifier(X)
+
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

twitter_model_91_5-.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3f12e3a20193609be0f8c8d6e2f5ca06e8a43e907b7d74dd108c3d64d77c5ad
+size 4757645