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--- |
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license: cc-by-4.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: inputs |
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dtype: string |
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- name: targets |
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dtype: string |
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- name: metadata |
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struct: |
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- name: locale |
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dtype: string |
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- name: example_id |
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dtype: string |
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- name: seeded_lists |
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list: |
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- name: name |
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dtype: string |
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- name: items |
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sequence: string |
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- name: seeded_notes |
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list: |
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- name: name |
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dtype: string |
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- name: content |
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dtype: string |
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- name: seeded_contacts |
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sequence: string |
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- name: previous_turns |
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list: |
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- name: user_query |
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dtype: string |
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- name: response_text |
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dtype: string |
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- name: linguistic_phenomena |
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dtype: string |
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- name: split |
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dtype: string |
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- name: context |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 24777921 |
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num_examples: 33577 |
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download_size: 6999588 |
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dataset_size: 24777921 |
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language: |
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- en |
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--- |
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Code to test on Colab |
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!pip install -q transformers[torch] tokenizers datasets evaluate rouge_score sentencepiece huggingface_hub --upgrade |
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from huggingface_hub import notebook_login |
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notebook_login() |
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import nltk |
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from datasets import load_dataset |
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import evaluate |
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import numpy as np |
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from transformers import T5Tokenizer, DataCollatorForSeq2Seq |
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from transformers import T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer |
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# Load and split the dataset |
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dataset = load_dataset("ajsbsd/presto") |
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dataset = dataset["train"].train_test_split(test_size=0.2) |
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#dataset = load_dataset("csv", data_files="./JEOPARDY_CSV.csv") |
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#dataset = dataset["train"].train_test_split(test_size=0.2) |
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# Load the tokenizer, model, and data collator |
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tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small") |
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model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small") |
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data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model) |
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# We prefix our tasks with "answer the question" |
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prefix = "answer the question: " |
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# Define our preprocessing function |
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def preprocess_function(examples): |
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"""Add prefix to the sentences, tokenize the text, and set the labels""" |
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# The "inputs" are the tokenized answer: |
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inputs = [prefix + doc for doc in examples["inputs"]] |
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model_inputs = tokenizer(inputs, max_length=128, truncation=True) |
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# The "labels" are the tokenized outputs: |
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labels = tokenizer(text_target=examples["targets"], max_length=512, truncation=True) |
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model_inputs["labels"] = labels["input_ids"] |
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return model_inputs |
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# Map the preprocessing function across our dataset |
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tokenized_dataset = dataset.map(preprocess_function, batched=True) |
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# Set up Rouge score for evaluation |
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nltk.download("punkt", quiet=True) |
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metric = evaluate.load("rouge") |
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def compute_metrics(eval_preds): |
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preds, labels = eval_preds |
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# decode preds and labels |
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labels = np.where(labels != -100, labels, tokenizer.pad_token_id) |
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decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) |
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decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) |
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# rougeLSum expects newline after each sentence |
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decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds] |
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decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels] |
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result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True) |
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return result |
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# Set up training arguments |
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training_args = Seq2SeqTrainingArguments( |
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output_dir="./results", |
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evaluation_strategy="epoch", |
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learning_rate=3e-4, |
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per_device_train_batch_size=8, |
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per_device_eval_batch_size=4, |
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weight_decay=0.01, |
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save_total_limit=3, |
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num_train_epochs=2, |
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predict_with_generate=True, |
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push_to_hub=False |
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) |
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# Set up trainer |
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trainer = Seq2SeqTrainer( |
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model=model, |
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args=training_args, |
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train_dataset=tokenized_dataset["train"], |
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eval_dataset=tokenized_dataset["test"], |
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tokenizer=tokenizer, |
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data_collator=data_collator, |
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compute_metrics=compute_metrics |
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) |
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# Train the model |
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trainer.train() |
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# Push to HF :) |
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trainer.push_to_hub() |
