initial commit

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README.md

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+---
+title: Arxiv_paper_classifier
+emoji: 📉
+colorFrom: gray
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.2.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+

app.py

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+import streamlit as st
+import transformers
+import pickle
+import seaborn as sns
+from pandas import DataFrame
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+st.markdown("# Hello, friend!")
+st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")
+# st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
+
+st.write("Loading tokenizer and dict")
+model_name_global = "allenai/scibert_scivocab_uncased"
+tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
+with open('./models/scibert/decode_dict.pkl', 'rb') as f:
+    decode_dict = pickle.load(f)
+
+with st.form(key="my_form"):
+    st.markdown("### 🎈 Do you want a little magic?  ")
+    st.markdown(" Write your article title and abstract to textboxes bellow and I'll gues topic of your paper!  ")
+    # ce, c1, ce, c2, c3 = st.columns([0.07, 1, 0.07, 5, 0.07])
+    ce, c2, c3 = st.columns([0.07, 5, 0.07])
+    # with c1:
+    #     ModelType = st.radio(
+    #         "Choose your model",
+    #         ["DistilBERT (Default)", "Flair"],
+    #         help="At present, you can choose between 2 models (Flair or DistilBERT) to embed your text. More to come!",
+    #     )
+    #
+    #     if ModelType == "Default (DistilBERT)":
+    #         # kw_model = KeyBERT(model=roberta)
+    #
+    #         @st.cache(allow_output_mutation=True)
+    #         def load_model():
+    #             return KeyBERT(model=roberta)
+    #
+    #
+    #         kw_model = load_model()
+    #
+    #     else:
+    #         @st.cache(allow_output_mutation=True)
+    #         def load_model():
+    #             return KeyBERT("distilbert-base-nli-mean-tokens")
+    #
+    #
+    #         kw_model = load_model()
+
+    with c2:
+        doc_title = st.text_area(
+            "Paste your abstract title below (max 100 words)",
+            height=210,
+        )
+
+        doc_abstract = st.text_area(
+            "Paste your abstract text below (max 100500 words)",
+            height=410,
+        )
+
+        MAX_WORDS_TITLE, MAX_WORDS_ABSTRACT = 50, 500
+        import re
+
+        len_title = len(re.findall(r"\w+", doc_title))
+        len_abstract = len(re.findall(r"\w+", doc_abstract))
+        if len_title > MAX_WORDS_TITLE:
+            st.warning(
+                "⚠️ Your title contains "
+                + str(len_title)
+                + " words."
+                + " Only the first 50 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
+            )
+
+            doc_title = doc_title[:MAX_WORDS_TITLE]
+
+        if len_abstract > MAX_WORDS_ABSTRACT:
+            st.warning(
+                "⚠️ Your abstract contains "
+                + str(len_abstract)
+                + " words."
+                + " Only the first 50 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
+            )
+
+            doc_abstract = doc_abstract[:MAX_WORDS_ABSTRACT]
+
+        submit_button = st.form_submit_button(label="✨ Let's play, try it!")
+
+if not submit_button:
+    st.stop()
+
+
+#  allow_output_mutation=True
+@st.cache(suppress_st_warning=True)
+def load_model():
+    st.write("Loading big model")
+    return AutoModelForSequenceClassification.from_pretrained("models/scibert/")
+
+
+def make_predict(tokens, decode_dict):
+    # tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
+    # tokens = tokenizer_(title + abstract, return_tensors="pt")
+
+    model_ = load_model()
+    outs = model_(tokens.input_ids)
+
+    probs = outs["logits"].softmax(dim=-1).tolist()[0]
+    topic_probs = {}
+    for i, p in enumerate(probs):
+        if p > 0.1:
+            topic_probs[decode_dict[i]] = p
+    return topic_probs
+
+
+model_local = "models/scibert/"
+
+title = doc_title
+abstract = doc_abstract
+tokens = tokenizer_(title + abstract, return_tensors="pt")
+
+predicts = make_predict(model_name_global, model_local, tokens, decode_dict, title, abstract)
+
+st.markdown("## 🎈 Yor article probably about:  ")
+st.header("")
+
+df = (
+    DataFrame(predicts.items(), columns=["Topic", "Prob"])
+        .sort_values(by="Prob", ascending=False)
+        .reset_index(drop=True)
+)
+
+df.index += 1
+
+# Add styling
+cmGreen = sns.light_palette("green", as_cmap=True)
+cmRed = sns.light_palette("red", as_cmap=True)
+df = df.style.background_gradient(
+    cmap=cmGreen,
+    subset=[
+        "Prob",
+    ],
+)
+
+c1, c2, c3 = st.columns([1, 3, 1])
+
+format_dictionary = {
+    "Prob": "{:.1%}",
+}
+
+df = df.format(format_dictionary)
+
+with c2:
+    st.table(df)

model_api.py

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+#
+#
+# def make_predict(model_name_global, model_local, decode_dict, title, abstract):
+#     model_name_global="allenai/scibert_scivocab_uncased"
+#     model_local="scibert_trainer/checkpoint-2000/"
+#
+#     tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
+#     tokens = tokenizer_(title + abstract, return_tensors="pt")
+#     model_ = AutoModelForSequenceClassification.from_pretrained(model_local)
+#     outs = model_(tokens.input_ids)
+#
+#     probs = outs["logits"].softmax(dim=-1).tolist()[0]
+#     topic_probs = {}
+#     for i, p in enumerate(probs):
+#         if p > 0.1:
+#             topic_probs[decode_dict[i]] = p
+#     return topic_probs

models/scibert/config.json

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+{
+  "_name_or_path": "allenai/scibert_scivocab_uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
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+}

models/scibert/trainer_state.json

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+{
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+  "best_model_checkpoint": null,
+  "epoch": 2.6007802340702213,
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+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
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+}

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch