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| import streamlit as st | |
| import json | |
| import pandas as pd | |
| import torch | |
| import torch.nn.functional as F | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| all_tags = '["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","econ.GN","econ.TH","eess.AS","eess.IV","eess.SP","eess.SY","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.KT","math.LO","math.MG","math.MP","math.NA","math.NT","math.OA","math.OC","math.PR","math.QA","math.RA","math.RT","math.SG","math.SP","math.ST","astro-ph.CO","astro-ph.EP","astro-ph.GA","astro-ph.HE","astro-ph.IM","astro-ph.SR","cond-mat.dis-nn","cond-mat.mes-hall","cond-mat.mtrl-sci","cond-mat.other","cond-mat.quant-gas","cond-mat.soft","cond-mat.stat-mech","cond-mat.str-el","cond-mat.supr-con","gr-qc","hep-ex","hep-lat","hep-ph","hep-th","math-ph","nlin.AO","nlin.CD","nlin.CG","nlin.PS","nlin.SI","nucl-ex","nucl-th","physics.acc-ph","physics.ao-ph","physics.app-ph","physics.atm-clus","physics.atom-ph","physics.bio-ph","physics.chem-ph","physics.class-ph","physics.comp-ph","physics.data-an","physics.ed-ph","physics.flu-dyn","physics.gen-ph","physics.geo-ph","physics.hist-ph","physics.ins-det","physics.med-ph","physics.optics","physics.plasm-ph","physics.pop-ph","physics.soc-ph","physics.space-ph","quant-ph","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.MF","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"]' | |
| all_tags = json.loads(all_tags) | |
| st.set_page_config( | |
| page_title="Classify article", page_icon="🏴☠️") | |
| st.title('🏴☠️ Classify a scientific article') | |
| text = st.text_area("Type article title and/or summary", | |
| placeholder="Probabilistic Constructions of Computable Objects and a Computable Version of Lovasz Local Lemma", height=5, max_chars=4096) | |
| def get_tokenizer_and_model(): | |
| model_name = 'distilbert-base-uncased' | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(all_tags)) | |
| with open('distilbert.pt', 'rb') as f: | |
| sd = torch.load(f, map_location=torch.device('cpu')) | |
| model.load_state_dict(sd) | |
| model.eval() | |
| return tokenizer, model | |
| if text: | |
| tokenizer, model = get_tokenizer_and_model() | |
| tokenized = tokenizer([text], padding=True, truncation=True, return_tensors="pt") | |
| prob = model(**tokenized)['logits'] | |
| prob = F.sigmoid(prob) | |
| df = pd.DataFrame(data=prob.detach().numpy().reshape(-1,1), index=all_tags, columns=['probability']) | |
| df=df.sort_values(by='probability', ascending=False) | |
| total_prob = 0 | |
| max_i = df.shape[0] | |
| for idx, p in enumerate(df['probability']): | |
| total_prob += p | |
| if total_prob > 0.95: | |
| max_i = idx + 1 | |
| df = df.head(max_i) | |
| st.table(df) |