---
license: cc-by-4.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: inputs
    dtype: string
  - name: targets
    dtype: string
  - name: metadata
    struct:
    - name: locale
      dtype: string
    - name: example_id
      dtype: string
    - name: seeded_lists
      list:
      - name: name
        dtype: string
      - name: items
        sequence: string
    - name: seeded_notes
      list:
      - name: name
        dtype: string
      - name: content
        dtype: string
    - name: seeded_contacts
      sequence: string
    - name: previous_turns
      list:
      - name: user_query
        dtype: string
      - name: response_text
        dtype: string
    - name: linguistic_phenomena
      dtype: string
    - name: split
      dtype: string
    - name: context
      dtype: string
  splits:
  - name: train
    num_bytes: 24777921
    num_examples: 33577
  download_size: 6999588
  dataset_size: 24777921
language:
- en
---

Code to test on Colab

!pip install -q transformers[torch] tokenizers datasets evaluate rouge_score sentencepiece huggingface_hub --upgrade

from huggingface_hub import notebook_login

notebook_login()

import nltk
from datasets import load_dataset
import evaluate
import numpy as np
from transformers import T5Tokenizer, DataCollatorForSeq2Seq
from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer

# Load and split the dataset
dataset = load_dataset("ajsbsd/presto")
dataset = dataset["train"].train_test_split(test_size=0.2)
#dataset = load_dataset("csv", data_files="./JEOPARDY_CSV.csv")
#dataset = dataset["train"].train_test_split(test_size=0.2)
# Load the tokenizer, model, and data collator
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)

# We prefix our tasks with "answer the question"
prefix = "answer the question: "

# Define our preprocessing function
def preprocess_function(examples):
    """Add prefix to the sentences, tokenize the text, and set the labels"""
    # The "inputs" are the tokenized answer:
    inputs = [prefix + doc for doc in examples["inputs"]]
    model_inputs = tokenizer(inputs, max_length=128, truncation=True)

    # The "labels" are the tokenized outputs:
    labels = tokenizer(text_target=examples["targets"], max_length=512, truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

# Map the preprocessing function across our dataset
tokenized_dataset = dataset.map(preprocess_function, batched=True)

# Set up Rouge score for evaluation
nltk.download("punkt", quiet=True)
metric = evaluate.load("rouge")

def compute_metrics(eval_preds):
    preds, labels = eval_preds

    # decode preds and labels
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    # rougeLSum expects newline after each sentence
    decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
    decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]

    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
    return result

# Set up training arguments
training_args = Seq2SeqTrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=3e-4,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=4,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=2,
    predict_with_generate=True,
    push_to_hub=False
)

# Set up trainer
trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics
)

# Train the model
trainer.train()

# Push to HF :)
trainer.push_to_hub()