deploy

app.py

@@ -0,0 +1,69 @@
+import numpy as np
+import pandas as pd
+import torch
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification
+)
+import streamlit as st
+
+DEPLOYMENT_PATH = '.'
+
+@st.cache_resource
+def setup():
+    model_name = 'distilbert-base-cased'
+    model = AutoModelForSequenceClassification.from_pretrained(f'{DEPLOYMENT_PATH}/checkpoint')
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    idx2category = pd.read_csv(f'{DEPLOYMENT_PATH}/categories.csv').values.squeeze()
+    return model, tokenizer, idx2category
+
+@torch.no_grad()
+def get_probas(title, abstract=None):
+    inputs = tokenizer(
+        title,
+        abstract,
+        padding=True,
+        truncation=True,
+        return_tensors='pt'
+    )
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probas = (
+        torch.sigmoid(logits)
+        .detach().numpy().reshape(-1)
+    )
+    return probas
+
+model, tokenizer, idx2category = setup()
+num_categories = len(idx2category)
+
+def get_categories_by_threshold(probas, threshold=0.3):
+    categories = [
+        idx2category[idx] for idx in range(num_categories)
+        if probas[idx] > threshold
+    ]
+    return categories
+
+def get_top_categories(probas, num_predictions=5):
+    categories = [
+        idx2category[idx] for idx in np.argsort(probas)[::-1][:num_predictions]
+    ]
+    return categories
+
+st.title('ArXiv Papers Categorization')
+title_input = st.text_input('Enter the title of paper:')
+abstract_input = st.text_area('Enter the abstract (optional):')
+
+IS_READY = len(title_input) > 0
+
+if IS_READY and st.button('Categorize'):
+    probas = get_probas(title_input, abstract_input)
+    categories_predicted = get_categories_by_threshold(probas)
+
+    if len(categories_predicted) == 0:
+        categories_predicted = get_top_categories(probas)
+
+    st.write('Relevant arXiv categories:')
+    for category in categories_predicted:
+        st.markdown(f'- `{category}`')

categories.csv

@@ -0,0 +1,122 @@
+category
+cond-mat.dis-nn
+cs.AI
+cs.AR
+cs.CC
+cs.CE
+cs.CG
+cs.CL
+cs.CR
+cs.CV
+cs.CY
+cs.DB
+cs.DC
+cs.DL
+cs.DM
+cs.DS
+cs.ET
+cs.FL
+cs.GL
+cs.GR
+cs.GT
+cs.HC
+cs.IR
+cs.IT
+cs.LG
+cs.LO
+cs.MA
+cs.MM
+cs.MS
+cs.NA
+cs.NE
+cs.NI
+cs.OH
+cs.OS
+cs.PF
+cs.PL
+cs.RO
+cs.SC
+cs.SD
+cs.SE
+cs.SI
+cs.SY
+econ.EM
+eess.AS
+eess.IV
+eess.SP
+math.AC
+math.AG
+math.AP
+math.AT
+math.CA
+math.CO
+math.CT
+math.CV
+math.DG
+math.DS
+math.FA
+math.GM
+math.GN
+math.GR
+math.GT
+math.HO
+math.IT
+math.LO
+math.MG
+math.MP
+math.NA
+math.NT
+math.OA
+math.OC
+math.PR
+math.QA
+math.RA
+math.RT
+math.SP
+math.ST
+nlin.AO
+nlin.CD
+nlin.CG
+nlin.PS
+physics.ao-ph
+physics.app-ph
+physics.bio-ph
+physics.chem-ph
+physics.class-ph
+physics.comp-ph
+physics.data-an
+physics.flu-dyn
+physics.gen-ph
+physics.geo-ph
+physics.hist-ph
+physics.ins-det
+physics.med-ph
+physics.optics
+physics.pop-ph
+physics.soc-ph
+physics.space-ph
+q-bio
+q-bio.BM
+q-bio.CB
+q-bio.GN
+q-bio.MN
+q-bio.NC
+q-bio.OT
+q-bio.PE
+q-bio.QM
+q-bio.SC
+q-bio.TO
+q-fin.CP
+q-fin.EC
+q-fin.GN
+q-fin.PM
+q-fin.PR
+q-fin.RM
+q-fin.ST
+q-fin.TR
+stat.AP
+stat.CO
+stat.ME
+stat.ML
+stat.OT
+stat.TH

checkpoint/config.json

@@ -0,0 +1,271 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
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+  "problem_type": "multi_label_classification",
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+  "tie_weights_": true,
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+  "vocab_size": 28996
+}

checkpoint/model.safetensors

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checkpoint/training_args.bin

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requirements.txt

@@ -0,0 +1,4 @@
+numpy
+pandas
+torch
+transformers