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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import nltk | |
| import math | |
| import torch | |
| model_name = "fabiochiu/t5-base-medium-title-generation" | |
| max_input_length = 512 | |
| st.header("Generate candidate titles for articles") | |
| st_model_load = st.text('Loading title generator model...') | |
| def load_model(): | |
| print("Loading model...") | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| nltk.download('punkt') | |
| print("Model loaded!") | |
| return tokenizer, model | |
| tokenizer, model = load_model() | |
| st.success('Model loaded!') | |
| st_model_load.text("") | |
| with st.sidebar: | |
| st.header("Model parameters") | |
| if 'num_titles' not in st.session_state: | |
| st.session_state.num_titles = 5 | |
| def on_change_num_titles(): | |
| st.session_state.num_titles = num_titles | |
| num_titles = st.slider("Number of titles to generate", min_value=1, max_value=10, value=1, step=1, on_change=on_change_num_titles) | |
| if 'temperature' not in st.session_state: | |
| st.session_state.temperature = 0.7 | |
| def on_change_temperatures(): | |
| st.session_state.temperature = temperature | |
| temperature = st.slider("Temperature", min_value=0.1, max_value=1.5, value=0.6, step=0.05, on_change=on_change_temperatures) | |
| st.markdown("_High temperature means that results are more random_") | |
| if 'text' not in st.session_state: | |
| st.session_state.text = "" | |
| st_text_area = st.text_area('Text to generate the title for', value=st.session_state.text, height=500) | |
| def generate_title(): | |
| st.session_state.text = st_text_area | |
| # tokenize text | |
| inputs = ["summarize: " + st_text_area] | |
| inputs = tokenizer(inputs, return_tensors="pt") | |
| # compute span boundaries | |
| num_tokens = len(inputs["input_ids"][0]) | |
| print(f"Input has {num_tokens} tokens") | |
| max_input_length = 500 | |
| num_spans = math.ceil(num_tokens / max_input_length) | |
| print(f"Input has {num_spans} spans") | |
| overlap = math.ceil((num_spans * max_input_length - num_tokens) / max(num_spans - 1, 1)) | |
| spans_boundaries = [] | |
| start = 0 | |
| for i in range(num_spans): | |
| spans_boundaries.append([start + max_input_length * i, start + max_input_length * (i + 1)]) | |
| start -= overlap | |
| print(f"Span boundaries are {spans_boundaries}") | |
| spans_boundaries_selected = [] | |
| j = 0 | |
| for _ in range(num_titles): | |
| spans_boundaries_selected.append(spans_boundaries[j]) | |
| j += 1 | |
| if j == len(spans_boundaries): | |
| j = 0 | |
| print(f"Selected span boundaries are {spans_boundaries_selected}") | |
| # transform input with spans | |
| tensor_ids = [inputs["input_ids"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected] | |
| tensor_masks = [inputs["attention_mask"][0][boundary[0]:boundary[1]] for boundary in spans_boundaries_selected] | |
| inputs = { | |
| "input_ids": torch.stack(tensor_ids), | |
| "attention_mask": torch.stack(tensor_masks) | |
| } | |
| # compute predictions | |
| outputs = model.generate(**inputs, do_sample=True, temperature=temperature) | |
| decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) | |
| predicted_titles = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs] | |
| st.session_state.titles = predicted_titles | |
| # generate title button | |
| st_generate_button = st.button('Generate title', on_click=generate_title) | |
| # title generation labels | |
| if 'titles' not in st.session_state: | |
| st.session_state.titles = [] | |
| if len(st.session_state.titles) > 0: | |
| with st.container(): | |
| st.subheader("Generated titles") | |
| for title in st.session_state.titles: | |
| st.markdown("__" + title + "__") |