metadata
datasets:
- gaussalgo/Canard_Wiki-augmented
- hotpot_qa
metrics:
- rouge
- bleu
model-index:
- name: T5-LM-Large_Canard-Fullwiki-HotpotQA-rephrase
results:
- task:
type: question-answering
name: Question Answering
dataset:
type: hotpot_qa
name: HotpotQA
split: validation
metrics:
- type: rouge
value: 0.4774
- type: bleu
value: 29.11
- task:
type: question-answering
name: Question Answering
dataset:
type: gaussalgo/Canard_Wiki-augmented
name: Wikipedia-augmented Conversational QA (Canard)
split: validation
metrics:
- type: rouge
value: 0.4377
- type: bleu
value: 19.34
license: cc-by-sa-4.0
language:
- en
Model Card for T5-LM-Large_Canard-HotpotQA-rephrase
This model is trained on three objectives:
- Generating answers for Canard dataset based on Wikipedia search results
- Generating answers for HotpotQA,
- Rephrasing questions by the conversation context.
Training
The model was trained using the following script, which can be copy-pasted and run as-is (with the installed requirements.txt).
All details, including the request format, can be inferred without errors from the code.
The best checkpoint was picked by a maximum ROUGE on Canard conversational QA's ROUGE.
import datasets
canard_train_augm = datasets.load_dataset("gaussalgo/Canard_Wiki-augmented", split="train")
canard_test_augm = datasets.load_dataset("gaussalgo/Canard_Wiki-augmented", split="test")
canard_df = canard_train_augm.to_pandas()
canard_test_df = canard_train_augm.to_pandas()
### Curation of seq2seq input contexts and labels
import random
def input_context_from_sample(row: dict, max_length=5) -> str:
context = "Previous conversation:"
context += "\nQuestion: "
context += ", ".join(row["History"][:3])
for i in range(3, len(row["History"]), 2):
context += "\nAnswer: "
context += row["History"][i]
if i+1 < len(row["History"]):
context += "\nQuestion: "
context += row["History"][i+1]
context += "\n\nCurrent Question: "
context += row["Question"]
context += "\nSearch results:"
all_contexts = row["retrieved_contexts"].tolist()[:max_length-1] + [row["true_contexts"]]
random.shuffle(all_contexts)
for i, search_result in enumerate(all_contexts):
context += "\n[%s]: " % (i+1)
context += search_result.replace("CANNOTANSWER", "")
context += "\nCurrent Answer: "
return context
def rephrasing_context_from_sample(row: dict) -> str:
context = "Previous conversation:"
context += "\nQuestion: "
context += ", ".join(row["History"][:3])
for i in range(3, len(row["History"]), 2):
context += "\nAnswer: "
context += row["History"][i]
if i+1 < len(row["History"]):
context += "\nQuestion: "
context += row["History"][i+1]
context += "\n\nCurrent Question: "
context += row["Question"]
context += "\nMore specific question: "
return context
def hotpotqa_context(row: dict) -> str:
context = "Current Question: "
context += row["question"]
context += "\nSearch results:"
all_contexts = [" ".join(context) for context in row["context"]["sentences"]]
for i, search_result in enumerate(all_contexts):
context += "\n[%s]: " % (i+1)
context += search_result.replace("CANNOTANSWER", "")
context += "\nCurrent Answer: "
return context
# Conversational QA sequences
input_texts = canard_df.apply(lambda row: input_context_from_sample(row), axis=1).values
input_val_texts = canard_test_df.iloc[:200].apply(lambda row: input_context_from_sample(row), axis=1).values
too_long_index = [len(t) > 20000 for t in input_texts]
input_texts = [t for i, t in enumerate(input_texts) if not too_long_index[i]]
# print(too_long_index)
print("training on %s samples" % len(input_texts))
labels = canard_df.answer.apply(lambda ans: "No answer" if ans == "CANNOTANSWER" else ans).values
labels = [l for i, l in enumerate(labels) if not too_long_index[i]]
val_labels = canard_test_df.answer.apply(lambda ans: "No answer" if ans == "CANNOTANSWER" else ans).values
# Rephrasing sequences
rephrasing_inputs = canard_df.apply(lambda row: rephrasing_context_from_sample(row), axis=1).values
rephrasing_val_inputs = canard_test_df.apply(lambda row: rephrasing_context_from_sample(row), axis=1).values
rephrasing_labels = canard_df.Rewrite.values
rephrasing_val_labels = canard_test_df.Rewrite.values
# HotpotQA sequences
hotpot_train = datasets.load_dataset("hotpot_qa", "distractor")["train"]
hotpot_val = datasets.load_dataset("hotpot_qa", "distractor")["validation"]
hotpot_inputs = hotpot_train.to_pandas().apply(hotpotqa_context, axis=1)
hotpot_val_inputs = hotpot_val.to_pandas().apply(hotpotqa_context, axis=1)
too_long_index = [len(t) > 20000 for t in hotpot_inputs]
hotpot_inputs = [t for i, t in enumerate(hotpot_inputs) if not too_long_index[i]]
hotpot_answers = [t for i, t in enumerate(hotpot_train["answer"]) if not too_long_index[i]]
# Training routine
# see Adaptor's homepage for details:
# https://github.com/gaussalgo/adaptor
# Base model
from adaptor.lang_module import LangModule
lang_module = LangModule("google/t5-large-lm-adapt")
from adaptor.evaluators.generative import ROUGE, BLEU
# Evaluations
evaluators = [BLEU(), ROUGE(decides_convergence=True)]
# Objectives
from adaptor.objectives.seq2seq import Sequence2Sequence
seq_qa = Sequence2Sequence(lang_module,
texts_or_path=input_texts,
labels_or_path=labels,
val_texts_or_path=input_val_texts,
val_labels_or_path=val_labels,
batch_size=4,
val_evaluators=evaluators,
objective_id="Canard")
seq_additional_qa = Sequence2Sequence(lang_module,
texts_or_path=hotpot_inputs,
labels_or_path=hotpot_answers,
val_texts_or_path=hotpot_val_inputs[:200],
val_labels_or_path=hotpot_val["answer"][:200],
batch_size=4,
val_evaluators=evaluators,
objective_id="HotpotQA",
share_other_objective_head=seq_qa)
seq_rephrasing = Sequence2Sequence(lang_module,
texts_or_path=rephrasing_inputs,
labels_or_path=rephrasing_labels,
val_texts_or_path=rephrasing_val_inputs[:200],
val_labels_or_path=rephrasing_val_labels[:200],
batch_size=4,
val_evaluators=evaluators,
objective_id="rephrasing",
share_other_objective_head=seq_qa)
# Training schedule & arguments
from adaptor.utils import AdaptationArguments, StoppingStrategy
training_arguments = AdaptationArguments(output_dir="checkpoints-chatbot",
learning_rate=5e-5,
stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED,
stopping_patience=8,
save_total_limit=8,
do_train=True,
do_eval=True,
bf16=True,
warmup_steps=1000,
gradient_accumulation_steps=8,
logging_steps=10,
eval_steps=200,
save_steps=1000,
num_train_epochs=10,
evaluation_strategy="steps")
from adaptor.schedules import ParallelSchedule
from adaptor.adapter import Adapter
schedule = ParallelSchedule(objectives=[seq_qa, seq_additional_qa, seq_rephrasing],
args=training_arguments)
adapter = Adapter(lang_module, schedule, args=training_arguments)
adapter.train() # Training for 63k updates
Usage
See the prompting templates used in training to infer the optimal prompting format.
Contact
Feel free to ask questions here, or at stefanik{at} gaussalgo.com