Upload 16 files

app.py

@@ -2,23 +2,20 @@ import streamlit as st
 import torch
 from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
 
-
-# Cache the model loading to speed up app restarts.
+# Кэширование загрузки модели для ускорения перезапуска приложения.
 @st.cache_resource
 def load_model_and_tokenizer():
     model = DistilBertForSequenceClassification.from_pretrained(
-        "./results/checkpoint-1980"
+        "./results/checkpoint-64800"
     )
     tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-cased")
     model.eval()
     return model, tokenizer
 
-
 model, tokenizer = load_model_and_tokenizer()
 
-
 def classify_text(text: str) -> str:
-    """Tokenize the text and run inference."""
+    """Токенизация текста и запуск инференса."""
     encoding = tokenizer(
         text, return_tensors="pt", padding=True, truncation=True, max_length=128
     )
@@ -26,24 +23,66 @@ def classify_text(text: str) -> str:
         outputs = model(**encoding)
         logits = outputs.logits
     predicted_class_id = torch.argmax(logits, dim=1).item()
-    id2label = model.config.id2label  # Assumes id2label was set during training.
-    predicted_label = (
-        id2label[predicted_class_id] if id2label else str(predicted_class_id)
-    )
+    id2label = model.config.id2label  # Предполагается, что id2label был задан при обучении.
+    predicted_label = id2label[predicted_class_id] if id2label else str(predicted_class_id)
     return predicted_label
 
+# Добавление кастомного CSS для стилизации в духе Артемия Лебедева.
+st.markdown(
+    """
+    <style>
+    @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700&display=swap');
 
-# Build the Streamlit interface.
-st.title("Text Classification with DistilBERT")
+    body {
+        background-color: #1a1a1a;
+        color: #f5f5f5;
+        font-family: 'Montserrat', sans-serif;
+    }
+    h1 {
+        text-align: center;
+        font-size: 3em;
+        font-weight: 700;
+        margin-bottom: 0.2em;
+    }
+    h2 {
+        text-align: center;
+        font-size: 1.5em;
+        font-weight: 400;
+        margin-bottom: 1em;
+    }
+    .stButton>button {
+        background-color: #ff4500;
+        color: white;
+        font-size: 18px;
+        font-weight: bold;
+        border: none;
+        border-radius: 5px;
+        padding: 10px 20px;
+    }
+    .stTextArea>div>div>textarea {
+        background-color: #333;
+        color: #f5f5f5;
+        border: 2px solid #ff4500;
+        border-radius: 5px;
+        padding: 10px;
+    }
+    </style>
+    """, unsafe_allow_html=True
+)
 
-st.write("Enter text in the box below and click 'Classify' to see the predicted label.")
+# Заголовки и описание приложения.
+st.markdown("<h1>Классификация тэгов в статьях архива</h1>", unsafe_allow_html=True)
+st.markdown("<h2>Дизайн почти как от студии Артемия Лебедева</h2>", unsafe_allow_html=True)
+st.write("Введите название статьи или её абстракт – и мы подберём подходящий тэг.")
 
-# Text input area.
-user_text = st.text_area("Input Text", "")
+# Текстовые поля для ввода названия и абстракта.
+title = st.text_area("Название", "")
+abstract = st.text_area("Абстракт", "")
 
-if st.button("Classify"):
+if st.button("Классифицировать"):
+    user_text = title.strip() + " " + abstract.strip()
     if user_text.strip() == "":
-        st.error("Please enter some text to classify.")
+        st.error("Пожалуйста, введите текст для классификации.")
     else:
         predicted_label = classify_text(user_text)
-        st.success(f"Predicted label: **{predicted_label}**")
+        st.success(f"Предсказанный тэг: **{predicted_label}**")

results/checkpoint-108000 copy/config.json

@@ -0,0 +1,333 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.3,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "cs.CC",
+    "1": "math.RT",
+    "2": "cs.LG",
+    "3": "math.LO",
+    "4": "math.RA",
+    "5": "stat.ML",
+    "6": "stat.ME",
+    "7": "math.CO",
+    "8": "q-fin.TR",
+    "9": "math.ST",
+    "10": "math.QA",
+    "11": "math.CV",
+    "12": "cs.DB",
+    "13": "physics.ins-det",
+    "14": "math.OA",
+    "15": "math.GM",
+    "16": "cs.IT",
+    "17": "cs.CL",
+    "18": "math.DG",
+    "19": "gr-qc",
+    "20": "math.OC",
+    "21": "cond-mat.mes-hall",
+    "22": "cs.NI",
+    "23": "astro-ph.SR",
+    "24": "hep-ph",
+    "25": "math.CA",
+    "26": "astro-ph.HE",
+    "27": "cs.CV",
+    "28": "cond-mat.mtrl-sci",
+    "29": "nucl-th",
+    "30": "cond-mat.stat-mech",
+    "31": "quant-ph",
+    "32": "math.NA",
+    "33": "math.DS",
+    "34": "physics.ao-ph",
+    "35": "cs.CG",
+    "36": "astro-ph.IM",
+    "37": "math.GR",
+    "38": "math-ph",
+    "39": "math.PR",
+    "40": "astro-ph.CO",
+    "41": "cs.DS",
+    "42": "math.AG",
+    "43": "cs.DL",
+    "44": "math.AC",
+    "45": "cs.SC",
+    "46": "cond-mat.supr-con",
+    "47": "physics.flu-dyn",
+    "48": "hep-ex",
+    "49": "cs.SY",
+    "50": "cond-mat.soft",
+    "51": "physics.class-ph",
+    "52": "cond-mat.str-el",
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+    "54": "math.GN",
+    "55": "cs.LO",
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+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.1",
+  "vocab_size": 28996
+}

results/checkpoint-108000 copy/model.safetensors

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results/checkpoint-64800/config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.3,
+  "hidden_dim": 3072,
+  "id2label": {
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+    "2": "cs.LG",
+    "3": "math.LO",
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