flan-t5-base-openbsd-faq

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

flan-t5-base-openbsd-faq

This model is a fine-tuned version of google/flan-t5-base fintuned on ajsbsd/openbsd-faq

These are questions from https://www.openbsd.org/faq/faq1.html for use on ajsbsd.net

It achieves the following results on the evaluation set:

• Loss: 2.2385
• Rouge1: 0.3935
• Rouge2: 0.3383
• Rougel: 0.3906
• Rougelsum: 0.3844

Model description

This model is a fine-tuned version of google/flan-t5-base

Intended uses & limitations

OpenBSD Q/A chat-bot.

Training and evaluation data

Questions created from https://www.openbsd.org/faq/faq1.html in Q/A format for text2text generation.

Training procedure

Trained at Google Colab with the following code.

!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/openbsd-faq")
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-base")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)

# We prefix our tasks with "answer the question"
prefix = "Please answer this 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["question"]]
    model_inputs = tokenizer(inputs, max_length=128, truncation=True)

    # The "labels" are the tokenized outputs:
    labels = tokenizer(text_target=examples["answer"], 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="./flan-t5-base-openbsd-faq",
    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=5,
    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()

trainer.push_to_hub()

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 8
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.14.7
• Tokenizers 0.15.0