Delete quick_ref.txt

quick_ref.txt

@@ -1,98 +0,0 @@
-https://huggingface.co/spaces/juliaannjose/hupd_patent_classifier/
-
-
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from datasets import load_dataset
-import pandas as pd
-
-
-# finetuned model
-language_model_path = "juliaannjose/finetuned_model"
-
-# load the dataset to
-# use the patent number, abstract and claim columns for UI
-with st.spinner("Loading..."):
-    dataset_dict = load_dataset(
-        "HUPD/hupd",
-        name="sample",
-        data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather",
-        icpr_label=None,
-        train_filing_start_date="2016-01-01",
-        train_filing_end_date="2016-01-21",
-        val_filing_start_date="2016-01-22",
-        val_filing_end_date="2016-01-31",
-    )
-    df_train = pd.DataFrame(dataset_dict["train"])
-    df_val = pd.DataFrame(dataset_dict["validation"])
-    df = pd.concat([df_train, df_val], ignore_index=True)
-
-
-# drop down menu with patent numbers
-_patent_id = st.selectbox(
-    "Select the Patent Number",
-    options=df["patent_number"],
-)
-
-
-# display abstract and claim
-def get_abs_claim(_pid):
-    # get abstract and claim corresponding to this patent id
-    _abs = df.loc[df["patent_number"] == _pid]["abstract"]
-    _cl = df.loc[df["patent_number"] == _pid]["claims"]
-    return _abs.values[0], _cl.values[0]
-
-
-_abstract, _claim = get_abs_claim(_patent_id)
-st.title("Abstract:")  # display abstract
-st.write(_abstract)
-st.title("Claim:")  # display claims
-st.write(_claim)
-
-
-# model and tokenizer initialization
-@st.cache_resource
-def load_model(language_model_path):
-    tokenizer = AutoTokenizer.from_pretrained(language_model_path)
-    model = AutoModelForSequenceClassification.from_pretrained(language_model_path)
-    return tokenizer, model
-
-
-tokenizer, model = load_model(language_model_path)
-# input to our model
-input_text = _abstract + _claim
-# get tokens
-inputs = tokenizer(
-    input_text,
-    truncation=True,
-    padding=True,
-    return_tensors="pt",
-)
-
-# get predictions
-id2label = {0: "REJECTED", 1: "ACCEPTED"}
-# when submit button clicked, run the model and get result
-if st.button("Submit"):
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probability = torch.nn.functional.softmax(outputs.logits, dim=1)
-
-    predicted_class_id = probability.argmax().item()
-    pred_label = id2label[predicted_class_id]
-    st.title("Predicted Patentability")
-    if probability[0][0] > probability[0][1]:
-        st.write("Rejection Score:")
-        st.write(probability[0][0].item())
-    else:
-        st.write("Acceptance Score:")
-        st.write(probability[0][1].item())
-    st.write("Result:", pred_label)
-
-
-
-
-
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-
-