Eli5 examples (#4968)
* add eli5 examples * add dense query script * query_di * merging * merging * add_utils * adds nearest neighbor wikipedia * batch queries * training_retriever * new notebooks * moved retriever traiing script * finished wiki40b * max_len_fix * train_s2s * retriever_batch_checkpointing * cleanup * merge * dim_fix * fix_indexer * fix_wiki40b_snippets * fix_embed_for_r * fp32 index * fix_sparse_q * joint_training * remove obsolete datasets * add_passage_nn_results * add_passage_nn_results * add_batch_nn * add_batch_nn * add_data_scripts * notebook * notebook * notebook * fix_multi_gpu * add_app * full_caching * full_caching * notebook * sparse_done * images * notebook * add_image_gif * with_Gif * add_contr_image * notebook * notebook * notebook * train_functions * notebook * min_retrieval_length * pandas_option * notebook * min_retrieval_length * notebook * notebook * eval_Retriever * notebook * images * notebook * add_example * add_example * notebook * fireworks * notebook * notebook * joe's notebook comments * app_update * notebook * notebook_link * captions * notebook * assing RetriBert model * add RetriBert to Auto * change AutoLMHead to AutoSeq2Seq * notebook downloads from hf models * style_black * style_black * app_update * app_update * fix_app_update * style * style * isort * Delete WikiELI5training.ipynb * Delete evaluate_eli5.py * Delete WikiELI5explore.ipynb * Delete ExploreWikiELI5Support.html * Delete explainlikeimfive.py * Delete wiki_snippets.py * children before parent * children before parent * style_black * style_black_only * isort * isort_new * Update src/transformers/modeling_retribert.py Co-authored-by: Julien Chaumond <chaumond@gmail.com> * typo fixes * app_without_asset * cleanup * Delete ELI5animation.gif * Delete ELI5contrastive.svg * Delete ELI5wiki_index.svg * Delete choco_bis.svg * Delete fireworks.gif * Delete huggingface_logo.jpg * Delete huggingface_logo.svg * Delete Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb * Delete eli5_app.py * Delete eli5_utils.py * readme * Update README.md * unused imports * moved_info * default_beam * ftuned model * disclaimer * Update src/transformers/modeling_retribert.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * black * add_doc * names * isort_Examples * isort_Examples * Add doc to index Co-authored-by: Julien Chaumond <chaumond@gmail.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
This commit is contained in:
Yacine Jernite
332
examples/longform-qa/eli5_app.py
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332
examples/longform-qa/eli5_app.py
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import numpy as np
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import torch
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import faiss
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import nlp
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import streamlit as st
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import transformers
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from elasticsearch import Elasticsearch
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from eli5_utils import (
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embed_questions_for_retrieval,
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make_qa_s2s_model,
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qa_s2s_generate,
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query_es_index,
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query_qa_dense_index,
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)
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from transformers import AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer
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MODEL_TYPE = "bart"
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LOAD_DENSE_INDEX = True
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@st.cache(allow_output_mutation=True)
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def load_models():
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if LOAD_DENSE_INDEX:
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qar_tokenizer = AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased")
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qar_model = AutoModel.from_pretrained("yjernite/retribert-base-uncased").to("cuda:0")
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_ = qar_model.eval()
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else:
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qar_tokenizer, qar_model = (None, None)
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if MODEL_TYPE == "bart":
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s2s_tokenizer = AutoTokenizer.from_pretrained("yjernite/bart_eli5")
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s2s_model = AutoModelForSeq2SeqLM.from_pretrained("yjernite/bart_eli5").to("cuda:0")
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save_dict = torch.load("seq2seq_models/eli5_bart_model_blm_2.pth")
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s2s_model.load_state_dict(save_dict["model"])
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_ = s2s_model.eval()
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else:
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s2s_tokenizer, s2s_model = make_qa_s2s_model(
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model_name="t5-small", from_file="seq2seq_models/eli5_t5_model_1024_4.pth", device="cuda:0"
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)
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return (qar_tokenizer, qar_model, s2s_tokenizer, s2s_model)
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@st.cache(allow_output_mutation=True)
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def load_indexes():
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if LOAD_DENSE_INDEX:
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faiss_res = faiss.StandardGpuResources()
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wiki40b_passages = nlp.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"]
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wiki40b_passage_reps = np.memmap(
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"wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat",
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dtype="float32",
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mode="r",
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shape=(wiki40b_passages.num_rows, 128),
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)
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wiki40b_index_flat = faiss.IndexFlatIP(128)
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wiki40b_gpu_index_flat = faiss.index_cpu_to_gpu(faiss_res, 1, wiki40b_index_flat)
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wiki40b_gpu_index_flat.add(wiki40b_passage_reps) # TODO fix for larger GPU
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else:
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wiki40b_passages, wiki40b_gpu_index_flat = (None, None)
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es_client = Elasticsearch([{"host": "localhost", "port": "9200"}])
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return (wiki40b_passages, wiki40b_gpu_index_flat, es_client)
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@st.cache(allow_output_mutation=True)
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def load_train_data():
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eli5 = nlp.load_dataset("eli5", name="LFQA_reddit")
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eli5_train = eli5["train_eli5"]
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eli5_train_q_reps = np.memmap(
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"eli5_questions_reps.dat", dtype="float32", mode="r", shape=(eli5_train.num_rows, 128)
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)
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eli5_train_q_index = faiss.IndexFlatIP(128)
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eli5_train_q_index.add(eli5_train_q_reps)
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return (eli5_train, eli5_train_q_index)
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passages, gpu_dense_index, es_client = load_indexes()
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qar_tokenizer, qar_model, s2s_tokenizer, s2s_model = load_models()
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eli5_train, eli5_train_q_index = load_train_data()
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def find_nearest_training(question, n_results=10):
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q_rep = embed_questions_for_retrieval([question], qar_tokenizer, qar_model)
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D, I = eli5_train_q_index.search(q_rep, n_results)
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nn_examples = [eli5_train[int(i)] for i in I[0]]
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return nn_examples
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def make_support(question, source="wiki40b", method="dense", n_results=10):
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if source == "none":
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support_doc, hit_lst = (" <P> ".join(["" for _ in range(11)]).strip(), [])
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else:
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if method == "dense":
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support_doc, hit_lst = query_qa_dense_index(
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question, qar_model, qar_tokenizer, passages, gpu_dense_index, n_results
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)
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else:
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support_doc, hit_lst = query_es_index(
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question, es_client, index_name="english_wiki40b_snippets_100w", n_results=n_results,
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)
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support_list = [
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(res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
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]
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question_doc = "question: {} context: {}".format(question, support_doc)
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return question_doc, support_list
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@st.cache(hash_funcs={torch.Tensor: (lambda _: None), transformers.tokenization_bart.BartTokenizer: (lambda _: None)})
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def answer_question(
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question_doc, s2s_model, s2s_tokenizer, min_len=64, max_len=256, sampling=False, n_beams=2, top_p=0.95, temp=0.8
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):
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with torch.no_grad():
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answer = qa_s2s_generate(
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question_doc,
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s2s_model,
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s2s_tokenizer,
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num_answers=1,
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num_beams=n_beams,
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min_len=min_len,
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max_len=max_len,
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do_sample=sampling,
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temp=temp,
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top_p=top_p,
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top_k=None,
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max_input_length=1024,
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device="cuda:0",
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)[0]
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return (answer, support_list)
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st.title("Long Form Question Answering with ELI5")
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# Start sidebar
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header_html = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
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header_full = """
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<html>
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<head>
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<style>
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.img-container {
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padding-left: 90px;
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padding-right: 90px;
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padding-top: 50px;
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padding-bottom: 50px;
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background-color: #f0f3f9;
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}
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</style>
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</head>
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<body>
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<span class="img-container"> <!-- Inline parent element -->
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%s
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</span>
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</body>
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</html>
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""" % (
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header_html,
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)
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st.sidebar.markdown(
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header_full, unsafe_allow_html=True,
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)
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# Long Form QA with ELI5 and Wikipedia
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description = """
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This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
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First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
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a pre-processed fixed snapshot of Wikipedia.
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"""
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st.sidebar.markdown(description, unsafe_allow_html=True)
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action_list = [
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"Answer the question",
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"View the retrieved document only",
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"View the most similar ELI5 question and answer",
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"Show me everything, please!",
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]
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demo_options = st.sidebar.checkbox("Demo options")
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if demo_options:
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action_st = st.sidebar.selectbox("", action_list, index=3,)
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action = action_list.index(action_st)
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show_type = st.sidebar.selectbox("", ["Show full text of passages", "Show passage section titles"], index=0,)
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show_passages = show_type == "Show full text of passages"
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else:
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action = 3
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show_passages = True
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retrieval_options = st.sidebar.checkbox("Retrieval options")
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if retrieval_options:
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retriever_info = """
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### Information retriever options
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The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
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trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
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The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
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"""
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st.sidebar.markdown(retriever_info)
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wiki_source = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
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index_type = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
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else:
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wiki_source = "wiki40b"
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index_type = "dense"
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sampled = "beam"
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n_beams = 2
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min_len = 64
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max_len = 256
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top_p = None
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temp = None
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generate_options = st.sidebar.checkbox("Generation options")
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if generate_options:
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generate_info = """
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### Answer generation options
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The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
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weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
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**beam** search, or **sample** from the decoder's output probabilities.
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"""
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st.sidebar.markdown(generate_info)
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sampled = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
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min_len = st.sidebar.slider(
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"Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
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)
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max_len = st.sidebar.slider(
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"Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
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)
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if sampled == "beam":
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n_beams = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
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else:
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top_p = st.sidebar.slider(
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"Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
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)
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temp = st.sidebar.slider(
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"Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
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)
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n_beams = None
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# start main text
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questions_list = [
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"<MY QUESTION>",
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"How do people make chocolate?",
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"Why do we get a fever when we are sick?",
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"How can different animals perceive different colors?",
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"What is natural language processing?",
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"What's the best way to treat a sunburn?",
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"What exactly are vitamins ?",
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"How does nuclear energy provide electricity?",
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"What's the difference between viruses and bacteria?",
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"Why are flutes classified as woodwinds when most of them are made out of metal ?",
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"Why do people like drinking coffee even though it tastes so bad?",
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"What happens when wine ages? How does it make the wine taste better?",
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"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
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"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
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"How does New Zealand have so many large bird predators?",
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]
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question_s = st.selectbox(
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"What would you like to ask? ---- select <MY QUESTION> to enter a new query", questions_list, index=1,
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)
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if question_s == "<MY QUESTION>":
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question = st.text_input("Enter your question here:", "")
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else:
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question = question_s
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if st.button("Show me!"):
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if action in [0, 1, 3]:
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if index_type == "mixed":
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_, support_list_dense = make_support(question, source=wiki_source, method="dense", n_results=10)
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_, support_list_sparse = make_support(question, source=wiki_source, method="sparse", n_results=10)
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support_list = []
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for res_d, res_s in zip(support_list_dense, support_list_sparse):
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if tuple(res_d) not in support_list:
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support_list += [tuple(res_d)]
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if tuple(res_s) not in support_list:
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support_list += [tuple(res_s)]
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support_list = support_list[:10]
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question_doc = "<P> " + " <P> ".join([res[-1] for res in support_list])
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else:
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question_doc, support_list = make_support(question, source=wiki_source, method=index_type, n_results=10)
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if action in [0, 3]:
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answer, support_list = answer_question(
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question_doc,
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s2s_model,
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s2s_tokenizer,
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min_len=min_len,
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max_len=int(max_len),
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sampling=(sampled == "sampled"),
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n_beams=n_beams,
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top_p=top_p,
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temp=temp,
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)
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st.markdown("### The model generated answer is:")
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st.write(answer)
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if action in [0, 1, 3] and wiki_source != "none":
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st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
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for i, res in enumerate(support_list):
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wiki_url = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
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sec_titles = res[1].strip()
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if sec_titles == "":
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sections = "[{}]({})".format(res[0], wiki_url)
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else:
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sec_list = sec_titles.split(" & ")
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sections = " & ".join(
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["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
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)
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st.markdown(
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"{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections),
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unsafe_allow_html=True,
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)
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if show_passages:
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st.write(
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'> <span style="font-family:arial; font-size:10pt;">' + res[-1] + "</span>", unsafe_allow_html=True
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)
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if action in [2, 3]:
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nn_train_list = find_nearest_training(question)
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train_exple = nn_train_list[0]
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st.markdown(
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"--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
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)
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answers_st = [
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"{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
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for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
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if i == 0 or sc > 2
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]
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st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
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disclaimer = """
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---
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**Disclaimer**
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*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
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Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
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Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
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"""
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st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
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