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patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16 patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16
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pytorch

tf

Contributed by

patrickvonplaten Patrick von Platen
24 models

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16") model = AutoModelForSeq2SeqLM.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16")

Shared Roberta2Roberta Summarization with πŸ€— EncoderDecoder Framework

This model is a shared Roberta2Roberta model, meaning that the encoder and decoder weights are tied, fine-tuned on summarization.

Roberta2Roberta is a EncoderDecoderModel, meaning that both the encoder and the decoder are roberta-base RoBERTa models. In this setup the encoder and decoder weights are tied. Leveraging the EncoderDecoderFramework, the two pretrained models can simply be loaded into the framework via:

roberta2roberta = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base", tie_encoder_decoder=True)

The decoder of an EncoderDecoder model needs cross-attention layers and usually makes use of causal masking for auto-regressiv generation. Thus, roberta2roberta is consequently fined-tuned on the CNN/Daily Maildataset and the resulting model roberta2roberta-share-cnn_dailymail-fp16 is uploaded here.

Example

The model is by no means a state-of-the-art model, but nevertheless produces reasonable summarization results. It was mainly fine-tuned as a proof-of-concept for the πŸ€— EncoderDecoder Framework.

The model can be used as follows:

from transformers import RobertaTokenizer, EncoderDecoderModel

model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16")
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")

article = """(CNN)Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members singing a racist chant. SAE's national chapter suspended the students, but University of Oklahoma President David B
oren took it a step further, saying the university's affiliation with the fraternity is permanently done. The news is shocking, but it's not the first time SAE has faced controversy. SAE was founded March 9, 185
6, at the University of Alabama, five years before the American Civil War, according to the fraternity website. When the war began, the group had fewer than 400 members, of which "369 went to war for the Confede
rate States and seven for the Union Army," the website says. The fraternity now boasts more than 200,000 living alumni, along with about 15,000 undergraduates populating 219 chapters and 20 "colonies" seeking fu
ll membership at universities. SAE has had to work hard to change recently after a string of member deaths, many blamed on the hazing of new recruits, SAE national President Bradley Cohen wrote in a message on t
he fraternity's website. The fraternity's website lists more than 130 chapters cited or suspended for "health and safety incidents" since 2010. At least 30 of the incidents involved hazing, and dozens more invol
ved alcohol. However, the list is missing numerous incidents from recent months. Among them, according to various media outlets: Yale University banned the SAEs from campus activities last month after members al
legedly tried to interfere with a sexual misconduct investigation connected to an initiation rite. Stanford University in December suspended SAE housing privileges after finding sorority members attending a frat
ernity function were subjected to graphic sexual content. And Johns Hopkins University in November suspended the fraternity for underage drinking. "The media has labeled us as the 'nation's deadliest fraternity,
' " Cohen said. In 2011, for example, a student died while being coerced into excessive alcohol consumption, according to a lawsuit. SAE's previous insurer dumped the fraternity. "As a result, we are paying Lloy
d's of London the highest insurance rates in the Greek-letter world," Cohen said. Universities have turned down SAE's attempts to open new chapters, and the fraternity had to close 12 in 18 months over hazing in
cidents."""

input_ids = tokenizer(article, return_tensors="pt").input_ids
output_ids = model.generate(input_ids)

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
# should produce
# SAE's national chapter suspended after video shows party-bound fraternity members singing racist chant. University of Oklahoma president says university's affiliation with fraternity is permanently done.
# SAE has had to close 12 chapters since 2010 after members were killed in hazing. The fraternity has had more than 130 chapters in 18 months.

Training script:

IMPORTANT: In order for this code to work, make sure you checkout to the branch more_general_trainer_metric, which slightly adapts the Trainer for EncoderDecoderModels according to this PR: https://github.com/huggingface/transformers/pull/5840.

The following code shows the complete training script that was used to fine-tune roberta2roberta-cnn_dailymail-fp16 for reproducability. The training last ~9h on a standard GPU.

#!/usr/bin/env python3
import nlp
import logging
from transformers import RobertaTokenizer, EncoderDecoderModel, Trainer, TrainingArguments

logging.basicConfig(level=logging.INFO)

model = EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base", tie_encoder_decoder=True)
tokenizer =  RobertaTokenizer.from_pretrained("roberta-base")

# load train and validation data
train_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="train")
val_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="validation[:5%]")

# load rouge for validation
rouge = nlp.load_metric("rouge", experiment_id=0)

# set decoding params
model.config.decoder_start_token_id = tokenizer.bos_token_id
model.config.eos_token_id = tokenizer.eos_token_id
model.config.max_length = 142
model.config.min_length = 56
model.config.no_repeat_ngram_size = 3
model.early_stopping = True
model.length_penalty = 2.0
model.num_beams = 4

encoder_length = 512
decoder_length = 128
batch_size = 16


# map data correctly
def map_to_encoder_decoder_inputs(batch):
    # Tokenizer will automatically set [BOS] <text> [EOS]
    # cut off at Longformer at 2048
    inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=encoder_length)
    # force summarization <= 256
    outputs = tokenizer(batch["highlights"], padding="max_length", truncation=True, max_length=decoder_length)

    batch["input_ids"] = inputs.input_ids
    batch["attention_mask"] = inputs.attention_mask
    batch["decoder_input_ids"] = outputs.input_ids
    batch["labels"] = outputs.input_ids.copy()
    # mask loss for padding
    batch["labels"] = [
        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
    ]
    batch["decoder_attention_mask"] = outputs.attention_mask

    assert all([len(x) == encoder_length for x in inputs.input_ids])
    assert all([len(x) == decoder_length for x in outputs.input_ids])

    return batch


def compute_metrics(pred):
    labels_ids = pred.label_ids
    pred_ids = pred.predictions

    # all unnecessary tokens are removed
    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
    labels_ids[labels_ids == -100] = tokenizer.eos_token_id
    label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)

    rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid

    return {
        "rouge2_precision": round(rouge_output.precision, 4),
        "rouge2_recall": round(rouge_output.recall, 4),
        "rouge2_fmeasure": round(rouge_output.fmeasure, 4),
    }


# make train dataset ready
train_dataset = train_dataset.map(
    map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
train_dataset.set_format(
    type="torch", columns=["input_ids", "attention_mask", "decoder_attention_mask", "decoder_input_ids", "labels"],
)

# same for validation dataset
val_dataset = val_dataset.map(
    map_to_encoder_decoder_inputs, batched=True, batch_size=batch_size, remove_columns=["article", "highlights"],
)
val_dataset.set_format(
    type="torch", columns=["input_ids", "decoder_attention_mask", "attention_mask", "decoder_input_ids", "labels"],
)

# set training arguments - these params are not really tuned, feel free to change
training_args = TrainingArguments(
    output_dir="./",
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    predict_from_generate=True,
    evaluate_during_training=True,
    do_train=True,
    do_eval=True,
    logging_steps=1000,
    save_steps=1000,
    eval_steps=1000,
    overwrite_output_dir=True,
    warmup_steps=2000,
    save_total_limit=3,
    fp16=True,
)

# instantiate trainer
trainer = Trainer(
    model=model,
    args=training_args,
    compute_metrics=compute_metrics,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

# start training
trainer.train()

Evaluation

The following script evaluates the model on the test set of CNN/Daily Mail.

#!/usr/bin/env python3
import nlp
from transformers import RobertaTokenizer, EncoderDecoderModel

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = EncoderDecoderModel.from_pretrained("patrickvonplaten/roberta2roberta-share-cnn_dailymail-fp16")
model.to("cuda")

test_dataset = nlp.load_dataset("cnn_dailymail", "3.0.0", split="test")
batch_size = 128


# map data correctly
def generate_summary(batch):
    # Tokenizer will automatically set [BOS] <text> [EOS]
    # cut off at BERT max length 512
    inputs = tokenizer(batch["article"], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
    input_ids = inputs.input_ids.to("cuda")
    attention_mask = inputs.attention_mask.to("cuda")

    outputs = model.generate(input_ids, attention_mask=attention_mask)

    # all special tokens including will be removed
    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)

    batch["pred"] = output_str

    return batch


results = test_dataset.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=["article"])

# load rouge for validation
rouge = nlp.load_metric("rouge")

pred_str = results["pred"]
label_str = results["highlights"]

rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=["rouge2"])["rouge2"].mid

print(rouge_output)

The obtained results should be:

- Rouge2 - mid -precision Rouge2 - mid - recall Rouge2 - mid - fmeasure
CNN/Daily Mail 15.6 18.79 16.59