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.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ title: Arxiv_paper_classifier
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+ emoji: 📉
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+ colorFrom: gray
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+ colorTo: pink
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+ sdk: streamlit
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+ sdk_version: 1.2.0
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+ app_file: app.py
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+ pinned: false
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+ license: apache-2.0
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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+
app.py ADDED
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+ import streamlit as st
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+ import transformers
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+ import pickle
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+ import seaborn as sns
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+ from pandas import DataFrame
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+
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+ st.markdown("# Hello, friend!")
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+ st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")
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+ # st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
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+
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+ st.write("Loading tokenizer and dict")
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+ model_name_global = "allenai/scibert_scivocab_uncased"
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+ tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
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+ with open('./models/scibert/decode_dict.pkl', 'rb') as f:
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+ decode_dict = pickle.load(f)
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+
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+ with st.form(key="my_form"):
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+ st.markdown("### 🎈 Do you want a little magic? ")
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+ st.markdown(" Write your article title and abstract to textboxes bellow and I'll gues topic of your paper! ")
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+ # ce, c1, ce, c2, c3 = st.columns([0.07, 1, 0.07, 5, 0.07])
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+ ce, c2, c3 = st.columns([0.07, 5, 0.07])
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+ # with c1:
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+ # ModelType = st.radio(
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+ # "Choose your model",
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+ # ["DistilBERT (Default)", "Flair"],
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+ # help="At present, you can choose between 2 models (Flair or DistilBERT) to embed your text. More to come!",
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+ # )
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+ #
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+ # if ModelType == "Default (DistilBERT)":
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+ # # kw_model = KeyBERT(model=roberta)
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+ #
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+ # @st.cache(allow_output_mutation=True)
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+ # def load_model():
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+ # return KeyBERT(model=roberta)
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+ #
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+ #
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+ # kw_model = load_model()
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+ #
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+ # else:
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+ # @st.cache(allow_output_mutation=True)
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+ # def load_model():
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+ # return KeyBERT("distilbert-base-nli-mean-tokens")
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+ #
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+ #
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+ # kw_model = load_model()
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+
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+ with c2:
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+ doc_title = st.text_area(
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+ "Paste your abstract title below (max 100 words)",
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+ height=210,
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+ )
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+
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+ doc_abstract = st.text_area(
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+ "Paste your abstract text below (max 100500 words)",
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+ height=410,
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+ )
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+
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+ MAX_WORDS_TITLE, MAX_WORDS_ABSTRACT = 50, 500
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+ import re
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+
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+ len_title = len(re.findall(r"\w+", doc_title))
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+ len_abstract = len(re.findall(r"\w+", doc_abstract))
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+ if len_title > MAX_WORDS_TITLE:
65
+ st.warning(
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+ "⚠️ Your title contains "
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+ + str(len_title)
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+ + " words."
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+ + " Only the first 50 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
70
+ )
71
+
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+ doc_title = doc_title[:MAX_WORDS_TITLE]
73
+
74
+ if len_abstract > MAX_WORDS_ABSTRACT:
75
+ st.warning(
76
+ "⚠️ Your abstract contains "
77
+ + str(len_abstract)
78
+ + " words."
79
+ + " Only the first 50 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
80
+ )
81
+
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+ doc_abstract = doc_abstract[:MAX_WORDS_ABSTRACT]
83
+
84
+ submit_button = st.form_submit_button(label="✨ Let's play, try it!")
85
+
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+ if not submit_button:
87
+ st.stop()
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+
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+
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+ # allow_output_mutation=True
91
+ @st.cache(suppress_st_warning=True)
92
+ def load_model():
93
+ st.write("Loading big model")
94
+ return AutoModelForSequenceClassification.from_pretrained("models/scibert/")
95
+
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+
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+ def make_predict(tokens, decode_dict):
98
+ # tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
99
+ # tokens = tokenizer_(title + abstract, return_tensors="pt")
100
+
101
+ model_ = load_model()
102
+ outs = model_(tokens.input_ids)
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+
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+ probs = outs["logits"].softmax(dim=-1).tolist()[0]
105
+ topic_probs = {}
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+ for i, p in enumerate(probs):
107
+ if p > 0.1:
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+ topic_probs[decode_dict[i]] = p
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+ return topic_probs
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+
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+
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+ model_local = "models/scibert/"
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+
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+ title = doc_title
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+ abstract = doc_abstract
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+ tokens = tokenizer_(title + abstract, return_tensors="pt")
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+
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+ predicts = make_predict(model_name_global, model_local, tokens, decode_dict, title, abstract)
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+
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+ st.markdown("## 🎈 Yor article probably about: ")
121
+ st.header("")
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+
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+ df = (
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+ DataFrame(predicts.items(), columns=["Topic", "Prob"])
125
+ .sort_values(by="Prob", ascending=False)
126
+ .reset_index(drop=True)
127
+ )
128
+
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+ df.index += 1
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+
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+ # Add styling
132
+ cmGreen = sns.light_palette("green", as_cmap=True)
133
+ cmRed = sns.light_palette("red", as_cmap=True)
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+ df = df.style.background_gradient(
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+ cmap=cmGreen,
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+ subset=[
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+ "Prob",
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+ ],
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+ )
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+
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+ c1, c2, c3 = st.columns([1, 3, 1])
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+
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+ format_dictionary = {
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+ "Prob": "{:.1%}",
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+ }
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+
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+ df = df.format(format_dictionary)
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+
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+ with c2:
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+ st.table(df)
model_api.py ADDED
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+ #
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+ #
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+ # def make_predict(model_name_global, model_local, decode_dict, title, abstract):
4
+ # model_name_global="allenai/scibert_scivocab_uncased"
5
+ # model_local="scibert_trainer/checkpoint-2000/"
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+ #
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+ # tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
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+ # tokens = tokenizer_(title + abstract, return_tensors="pt")
9
+ # model_ = AutoModelForSequenceClassification.from_pretrained(model_local)
10
+ # outs = model_(tokens.input_ids)
11
+ #
12
+ # probs = outs["logits"].softmax(dim=-1).tolist()[0]
13
+ # topic_probs = {}
14
+ # for i, p in enumerate(probs):
15
+ # if p > 0.1:
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+ # topic_probs[decode_dict[i]] = p
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+ # return topic_probs
models/.DS_Store ADDED
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models/scibert/config.json ADDED
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+ {
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+ "_name_or_path": "allenai/scibert_scivocab_uncased",
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+ "architectures": [
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+ "BertForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ }
models/scibert/decode_dict.pkl ADDED
Binary file (1.63 kB). View file
 
models/scibert/rng_state.pth ADDED
Binary file (19.5 kB). View file
 
models/scibert/scheduler.pt ADDED
Binary file (623 Bytes). View file
 
models/scibert/trainer_state.json ADDED
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1
+ {
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Binary file (2.99 kB). View file
 
requirements.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
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2
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