!pip install pytesseract transformers datasets rouge-score nltk tensorboard py7zr --upgrade# install git-fls for pushing model and logs to the hugging face hub!sudo apt-get install git-lfs --yes

from huggingface_hub import notebook_login notebook_login()

{  "id": "13818513",  "summary": "Amanda baked cookies and will bring Jerry some tomorrow.",  "dialogue": "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}

from datasets import load_dataset # Load dataset from the hubdataset = load_dataset(dataset_id) print(f"Train dataset size: {len(dataset['train'])}")print(f"Test dataset size: {len(dataset['test'])}") # Train dataset size: 14732# Test dataset size: 819

from random import randrange  sample = dataset['train'][randrange(len(dataset["train"]))]print(f"dialogue: \n{sample['dialogue']}\n---------------")print(f"summary: \n{sample['summary']}\n---------------")

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model_id="google/flan-t5-base" # Load tokenizer of FLAN-t5-basetokenizer = AutoTokenizer.from_pretrained(model_id) 

from datasets import concatenate_datasets # The maximum total input sequence length after tokenization.# Sequences longer than this will be truncated, sequences shorter will be padded.tokenized_inputs = concatenate_datasets([dataset["train"], dataset["test"]]).map(lambda x: tokenizer(x["dialogue"], truncation=True), batched=True, remove_columns=["dialogue", "summary"])max_source_length = max([len(x) for x in tokenized_inputs["input_ids"]])print(f"Max source length: {max_source_length}") # The maximum total sequence length for target text after tokenization.# Sequences longer than this will be truncated, sequences shorter will be padded."tokenized_targets = concatenate_datasets([dataset["train"], dataset["test"]]).map(lambda x: tokenizer(x["summary"], truncation=True), batched=True, remove_columns=["dialogue", "summary"])max_target_length = max([len(x) for x in tokenized_targets["input_ids"]])print(f"Max target length: {max_target_length}")

def preprocess_function(sample,padding="max_length"):    # add prefix to the input for t5    inputs = ["summarize: " + item for item in sample["dialogue"]]     # tokenize inputs    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)     # Tokenize targets with the `text_target` keyword argument    labels = tokenizer(text_target=sample["summary"], max_length=max_target_length, padding=padding, truncation=True)     # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore    # padding in the loss.    if padding == "max_length":        labels["input_ids"] = [            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]        ]     model_inputs["labels"] = labels["input_ids"]    return model_inputs tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=["dialogue", "summary", "id"])print(f"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}")

from transformers import AutoModelForSeq2SeqLM # huggingface hub model idmodel_id="google/flan-t5-base" # load model from the hubmodel = AutoModelForSeq2SeqLM.from_pretrained(model_id)

import evaluateimport nltkimport numpy as npfrom nltk.tokenize import sent_tokenizenltk.download("punkt") # Metricmetric = evaluate.load("rouge") # helper function to postprocess textdef postprocess_text(preds, labels):    preds = [pred.strip() for pred in preds]    labels = [label.strip() for label in labels]     # rougeLSum expects newline after each sentence    preds = ["\n".join(sent_tokenize(pred)) for pred in preds]    labels = ["\n".join(sent_tokenize(label)) for label in labels]     return preds, labels def compute_metrics(eval_preds):    preds, labels = eval_preds    if isinstance(preds, tuple):        preds = preds[0]    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)    # Replace -100 in the labels as we can't decode them.    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)     # Some simple post-processing    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)     result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)    result = {k: round(v * 100, 4) for k, v in result.items()}    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]    result["gen_len"] = np.mean(prediction_lens)    return result

from transformers import DataCollatorForSeq2Seq # we want to ignore tokenizer pad token in the losslabel_pad_token_id = -100# Data collatordata_collator = DataCollatorForSeq2Seq(    tokenizer,    model=model,    label_pad_token_id=label_pad_token_id,    pad_to_multiple_of=8) 

from huggingface_hub import HfFolderfrom transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments # Hugging Face repository idrepository_id = f"{model_id.split('/')[1]}-{dataset_id}" # Define training argstraining_args = Seq2SeqTrainingArguments(    output_dir=repository_id,    per_device_train_batch_size=8,    per_device_eval_batch_size=8,    predict_with_generate=True,    fp16=False, # Overflows with fp16    learning_rate=5e-5,    num_train_epochs=5,    # logging & evaluation strategies    logging_dir=f"{repository_id}/logs",    logging_strategy="steps",    logging_steps=500,    evaluation_strategy="epoch",    save_strategy="epoch",    save_total_limit=2,    load_best_model_at_end=True,    # metric_for_best_model="overall_f1",    # push to hub parameters    report_to="tensorboard",    push_to_hub=False,    hub_strategy="every_save",    hub_model_id=repository_id,    hub_token=HfFolder.get_token(),) # Create Trainer instancetrainer = Seq2SeqTrainer(    model=model,    args=training_args,    data_collator=data_collator,    train_dataset=tokenized_dataset["train"],    eval_dataset=tokenized_dataset["test"],    compute_metrics=compute_metrics,)

# Start trainingtrainer.train()

Nice, we have trained our model. 🎉 Lets run evaluate the best model again on the test set.

The best score we achieved is an rouge1 score of 47.23.

Lets save our results and tokenizer to the Hugging Face Hub and create a model card.

# Save our tokenizer and create model cardtokenizer.save_pretrained(repository_id)trainer.create_model_card()# Push the results to the hubtrainer.push_to_hub()

4. Run Inference and summarize ChatGPT dialogues

Now we have a trained model, we can use it to run inference. We will use the pipeline API from transformers and a test example from our dataset.

from transformers import pipelinefrom random import randrange # load model and tokenizer from huggingface hub with pipelinesummarizer = pipeline("summarization", model="philschmid/flan-t5-base-samsum", device=0) # select a random test samplesample = dataset['test'][randrange(len(dataset["test"]))]print(f"dialogue: \n{sample['dialogue']}\n---------------") # summarize dialogueres = summarizer(sample["dialogue"]) print(f"flan-t5-base summary:\n{res[0]['summary_text']}")

output

    dialogue:    Abby: Have you talked to Miro?    Dylan: No, not really, I've never had an opportunity    Brandon: me neither, but he seems a nice guy    Brenda: you met him yesterday at the party?    Abby: yes, he's so interesting    Abby: told me the story of his father coming from Albania to the US in the early 1990s    Dylan: really, I had no idea he is Albanian    Abby: he is, he speaks only Albanian with his parents    Dylan: fascinating, where does he come from in Albania?    Abby: from the seacoast    Abby: Duress I believe, he told me they are not from Tirana    Dylan: what else did he tell you?    Abby: That they left kind of illegally    Abby: it was a big mess and extreme poverty everywhere    Abby: then suddenly the border was open and they just left    Abby: people were boarding available ships, whatever, just to get out of there    Abby: he showed me some pictures, like <file_photo>    Dylan: insane    Abby: yes, and his father was among the people    Dylan: scary but interesting    Abby: very!    ---------------    flan-t5-base summary:    Abby met Miro yesterday at the party. Miro's father came from Albania to the US in the early 1990s. He speaks Albanian with his parents. The border was open and people were boarding ships to get out of there.

Thanks for reading! If you have any questions, feel free to contact me on Twitter or LinkedIn.