--- title: modeling.summarization keywords: fastai sidebar: home_sidebar summary: "This module contains custom models, loss functions, custom splitters, etc... summarization tasks." description: "This module contains custom models, loss functions, custom splitters, etc... summarization tasks." nb_path: "nbs/02e_modeling-summarization.ipynb" ---
{% raw %}
{% endraw %} {% raw %}
{% endraw %} {% raw %}
torch.cuda.set_device(1)
print(f'Using GPU #{torch.cuda.current_device()}: {torch.cuda.get_device_name()}')
Using GPU #1: GeForce GTX 1080 Ti
{% endraw %}

Summarization

The objective of summarization is to generate a concise and accurate representation of a much larger body of text. For example, we may want to summarize an article in a single sentence.

{% raw %}
path = Path('./')
cnndm_df = pd.read_csv(path/'cnndm_sample.csv'); len(cnndm_df)
1000
{% endraw %} {% raw %}
cnndm_df.head(2)
article highlights ds_type
0 (CNN) -- Globalization washes like a flood over the world's cultures and economies. Floods can be destructive; however, they can also bring blessings, as the annual floods of the Nile did for ancient Egypt. The world's great universities can be crucial instruments in shaping, in a positive way, humankind's reaction to globalization and the development of humankind itself. Traditionally, universities have been defined and limited by location, creating an academic community and drawing students and scholars to that place. Eventually, some universities began to encourage students to study el... John Sexton: Traditionally, universities have been defined and limited by location .\nGlobal campuses form a network of thought, innovation, he writes .\nFaculty can teach, Sexton says, students can team up in many cities at once .\nSexton: Research, scholarship can be shared and cultural ties made in "century of knowledge" train
1 (CNN) -- Armenian President Robert Kocharian declared a state of emergency Saturday night after a day of clashes between police and protesters, a spokeswoman for the Armenian Foreign Ministry said. Opposition supporters wave an Armenian flag during a protest rally in Yerevan, Armenia, on Saturday. The protesters claim last month's presidential election was rigged. The state of emergency will "hopefully bring some order" to the capital, Yerevan, said Salpi Ghazarian, assistant to the Armenian foreign minister, who spoke to CNN early Sunday. The state of emergency could last until March 20, ... NEW: Protest moves after crackdown at Freedom Square .\nOrder sought after protests over last month's election turn violent .\nDemonstrators say the election was fraudulent .\nState of emergency could last until March 20, official says . train
{% endraw %} {% raw %}
pretrained_model_name = "facebook/bart-large-cnn"
hf_arch, hf_config, hf_tokenizer, hf_model = BLURR_MODEL_HELPER.get_hf_objects(pretrained_model_name, 
                                                                               model_cls=BartForConditionalGeneration)

hf_arch, type(hf_config), type(hf_tokenizer), type(hf_model)
('bart',
 transformers.configuration_bart.BartConfig,
 transformers.tokenization_bart.BartTokenizer,
 transformers.modeling_bart.BartForConditionalGeneration)
{% endraw %} {% raw %}
hf_batch_tfm = HF_SummarizationBatchTransform(hf_arch, hf_tokenizer, max_length=[256, 130])

blocks = (HF_TextBlock(hf_batch_tfm=hf_batch_tfm), noop)

dblock = DataBlock(blocks=blocks, 
                   get_x=ColReader('article'), 
                   get_y=ColReader('highlights'), 
                   splitter=RandomSplitter())
{% endraw %} {% raw %}
dls = dblock.dataloaders(cnndm_df, bs=2)
{% endraw %} {% raw %}
b = dls.one_batch()
{% endraw %} {% raw %}
len(b), b[0]['input_ids'].shape, b[1].shape
(2, torch.Size([2, 256]), torch.Size([2, 69]))
{% endraw %} {% raw %}
dls.show_batch(dataloaders=dls, max_n=2)
text target
0 (CNN) -- Home to up to 10 percent of all known species, Mexico is recognized as one of the most biodiverse regions on the planet. The twin threats of climate change and human encroachment on natural environments are, however, threatening the existence of the country's rich wildlife. And there is a great deal to lose. In the United Nations Environment Program (UNEP) World Conservation Monitoring Centre's list of megadiverse countries Mexico ranks 11th. The list represents a group of 17 countries that harbor the majority of the Earth's species and are therefore considered extremely biodiverse. From its coral reefs in the Caribbean Sea to its tropical jungles in Chiapas and the Yucatan peninsula and its deserts and prairies in the north, Mexico boasts an incredibly rich variety of flora and fauna. Some 574 out of 717 reptile species found in Mexico -- the most in any country -- can only be encountered within its borders. It is home to 502 types of mammals, 290 species of birds, 1,150 varieties of birds and 26,000 classifications of plants. Pronatura, a non-profit organization that works to promote conservation and sustainable development in Mexico, has selected six species which it says symbolize the problems faced by the Mexico hosts to up to 10 percent of all known species on Earth.\nIt is home to 502 types of mammals, 290 bird species and 26,000 types of plants.\nHuman development and climate change is placing a big strain on its biodiversity.\nThe Golden Eagle is under threat in spite of being the country's national symbol.
1 (CNN) -- Wondering where to go for your next holiday? Experts explain which destinations we should be checking out in 2014. Brazil: The World Cup. The modern game of football, or soccer, may have been born in England's public schools, but many will claim its soul has settled in Brazil. It has the world's most successful international team, winning the World Cup five times. It calls what many claim to be the world's greatest player, Pele, one of its own. And company managers and bosses are known to demand their employees skip work to watch the big games. The World Cup next year then, to be hosted over June and July in 18 cities across the country, is likely to be memorable, to say the least. Throw in the rest of the country, which includes rainforests, beaches and a party culture that makes most New Year's Eve soirees look decidedly po-faced, and you have the makings of an epic trip. Daisy Parker from ABTA (Association of British Travel Agents) suggests visiting some of the country's more remote areas. "The Brazilians' love of a party is going to make it a must-visit long haul destination for 2014," she says. "Once fans have flown New Zealand government threw $50 million into the construction of the Nga Haerenga cycle trails.\nNosara in Costa Rica recently awarded a Blue Flag -- a certification awarded to world's best beaches.\nFirst few months of 2014 best period for Northern Lights for years, as NASA scientists predict a peak in solar activity.
{% endraw %}

Metrics

In this section, we'll add helpful metrics for summarization tasks

{% raw %}
{% endraw %} {% raw %}

calculate_rouge[source]

calculate_rouge(predicted_txts, reference_txts, rouge_keys=['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)

{% endraw %}

Training

Here we create a summarization specific subclass of HF_BaseModelCallback in order to include custom, summarization specific, metrics, and also handle the pre-calculated loss during training

{% raw %}
{% endraw %} {% raw %}

class HF_SummarizationModelCallback[source]

HF_SummarizationModelCallback(rouge_metrics=['rouge1', 'rouge2', 'rougeL'], text_gen_kwargs={}, **kwargs) :: HF_BaseModelCallback

Basic class handling tweaks of the training loop by changing a Learner in various events

{% endraw %}

We add a custom param splitter to give us a bit more depth in applying discriminative learning rates for summarization.

{% raw %}
{% endraw %} {% raw %}

summarization_splitter[source]

summarization_splitter(m, arch)

Custom param splitter for summarization models

{% endraw %}

Even though we don't really need a loss function, we have to provide a custom loss class/function for fastai to function properly (e.g. one with a decodes and activation methods). Why? Because these methods will get called in methods like show_results to get the actual predictions.

{% raw %}
{% endraw %} {% raw %}

class HF_MaskedLMLoss[source]

HF_MaskedLMLoss()

{% endraw %} {% raw %}
text_gen_kwargs = { **hf_config.task_specific_params['summarization'], **{'max_length': 130, 'min_length': 30} }
text_gen_kwargs
{'early_stopping': True,
 'length_penalty': 2.0,
 'max_length': 130,
 'min_length': 30,
 'no_repeat_ngram_size': 3,
 'num_beams': 4}
{% endraw %} {% raw %}
model = HF_BaseModelWrapper(hf_model)
model_cb = HF_SummarizationModelCallback(text_gen_kwargs=text_gen_kwargs)

learn = Learner(dls, 
                model,
                opt_func=ranger,
                loss_func=HF_MaskedLMLoss(),
                cbs=[model_cb],
                splitter=partial(summarization_splitter, arch=hf_arch))#.to_fp16()

learn.create_opt() 
learn.freeze()
{% endraw %} {% raw %}
 
{% endraw %} {% raw %}
b = dls.one_batch()
preds = learn.model(b[0])
len(preds),preds[0], preds[1].shape
(3,
 tensor(3.4781, device='cuda:1', grad_fn=<NllLossBackward>),
 torch.Size([2, 68, 50264]))
{% endraw %} {% raw %}
len(b), len(b[0]), b[0]['input_ids'].shape, len(b[1]), b[1].shape
(2, 4, torch.Size([2, 256]), 2, torch.Size([2, 69]))
{% endraw %} {% raw %}
print(len(learn.opt.param_groups))
3
{% endraw %} {% raw %}
learn.lr_find(suggestions=True)
SuggestedLRs(lr_min=0.0003019951749593019, lr_steep=0.00013182566908653826)
{% endraw %} {% raw %}
learn.fit_one_cycle(1, lr_max=4e-5)
epoch train_loss valid_loss rouge1 rouge2 rougeL time
0 1.746875 1.719614 0.384311 0.166805 0.264810 03:52
{% endraw %}

Showing results

Below we'll add in additional functionality to take advantage of huggingface's PreTrainedModel.generate model, which can be used to easily implement beam search, top-k/nucleous sampling, etc... so that we get more human sounding results.

{% raw %}
test_article = """
About 10 men armed with pistols and small machine guns raided a casino in Switzerland and made off 
into France with several hundred thousand Swiss francs in the early hours of Sunday morning, police said. 
The men, dressed in black clothes and black ski masks, split into two groups during the raid on the Grand Casino 
Basel, Chief Inspector Peter Gill told CNN. One group tried to break into the casino's vault on the lower level 
but could not get in, but they did rob the cashier of the money that was not secured, he said. The second group 
of armed robbers entered the upper level where the roulette and blackjack tables are located and robbed the 
cashier there, he said. As the thieves were leaving the casino, a woman driving by and unaware of what was 
occurring unknowingly blocked the armed robbers' vehicles. A gunman pulled the woman from her vehicle, beat 
her, and took off for the French border. The other gunmen followed into France, which is only about 100 
meters (yards) from the casino, Gill said. There were about 600 people in the casino at the time of the robbery. 
There were no serious injuries, although one guest on the Casino floor was kicked in the head by one of the 
robbers when he moved, the police officer said. Swiss authorities are working closely with French authorities, 
Gill said. The robbers spoke French and drove vehicles with French license plates. CNN's Andreena Narayan 
contributed to this report.
"""
{% endraw %} {% raw %}
res = learn.blurr_predict(test_article)
print(hf_tokenizer.decode(res[0][:20]))
 10 10About 10 men with with pistols and machine machine guns raid a casino in Switzerland and made off
{% endraw %}

That doesn't look much like a human-generated summary. Let's use huggingface's PreTrainedModel.generate method to create something more human-like.

{% raw %}
b = dls.valid.one_batch()

test_input_ids = b[0]['input_ids'][0].unsqueeze(0).to(learn.model.hf_model.device)
test_trg_ids = b[1][0].unsqueeze(0).to(learn.model.hf_model.device)

gen_text = learn.model.hf_model.generate(test_input_ids, num_beams=4, max_length=130, min_length=30)

print('=== Target ===')
print(f'{hf_tokenizer.decode(test_trg_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)}\n')

print('=== Prediction ===')
print(hf_tokenizer.decode(gen_text[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
=== Target ===
 NEW: Planes depart Australia to resume their search for airplane debris.
NEW: Official: Passengers' relatives are moved to a different Kuala Lumpur hotel.
Objects seen on satellite spark intensive search in southern Indian Ocean.
U.S. officials: Files were deleted from flight simulator's hard drive after February 3.

=== Prediction ===
 NEW: Australian military planes resume their search for two objects detected by satellite over 1,400 miles off Australia's west coast.
The search has covered a mind-boggling 2.97 million square miles, which is nearly the size of the continental United States.
An exhaustive search has yielded some clues, but no proof of where the Boeing 777 is or definitively what happened to it.
Malaysia Airlines Flight 370 disappeared on March 8 with 227 passengers and 12 crew members aboard.
{% endraw %}

We'll add a blurr_summarize method to Learner that uses huggingface's PreTrainedModel.generate to create our predictions. For the full list of arguments you can pass in see here. You can also check out their "How To Generate" notebook for more information about how it all works.

{% raw %}
{% endraw %} {% raw %}

Learner.blurr_summarize[source]

Learner.blurr_summarize(inp, **kwargs)

Uses the built-in generate method to generate the text (see here for a list of arguments you can pass in)

{% endraw %} {% raw %}
outputs = learn.blurr_summarize(test_article, num_return_sequences=3)

for idx, o in enumerate(outputs):
    print(f'=== Prediction {idx+1} ===\n{o}\n')
=== Prediction 1 ===
 Police: About 10 men with pistols and machine guns raid a casino in Switzerland and make off with several hundred thousand Swiss francs .
The men, dressed in black clothes and black ski masks, split into two groups during the raid on the Grand Casino Basel .
One group tried to break into the casino's vault on the lower level but could not get in .                The second group entered the upper level where the roulette and blackjack tables are located and robbed the cashier there .                There were no serious injuries, although one guest on the Casino floor was kicked in the head by one of the robbers when he moved .

=== Prediction 2 ===
 Police: About 10 men with pistols and machine guns raid a casino in Switzerland and make off with several hundred thousand Swiss francs .
The men, dressed in black clothes and black ski masks, split into two groups during the raid on the Grand Casino Basel .
One group tried to break into the casino's vault on the lower level but could not get in .                The second group entered the upper level where the roulette and blackjack tables are located and robbed the cashier there .
There were no serious injuries, although one guest on the Casino floor was kicked in the head by one of the robbers .

=== Prediction 3 ===
 Police: About 10 men with pistols and machine guns raid a casino in Switzerland and make off with several hundred thousand Swiss francs .
The men, dressed in black clothes and black ski masks, split into two groups during the raid on the Grand Casino Basel .
One group tried to break into the casino's vault on the lower level but could not get in .                The second group entered the upper level where the roulette and blackjack tables are located and robbed the cashier there .                There were no serious injuries, although one guest on the Casino floor was kicked in the head by one of the robbers .

{% endraw %}

Much nicer!!! Now, we can update our @typedispatched show_results to use this new method.

{% raw %}
{% endraw %} {% raw %}
learn.show_results(learner=learn, input_trunc_at=500, target_trunc_at=250)
text target prediction
0 (CNN) -- Two weeks. Two gut-wrenching, frustrating, mysterious weeks. That's how long it's been since 227 passengers and 12 crew members boarded Malaysia Airlines Flight 370, destined for Beijing. A routine trip, it seemed, to catch up relatives in time for the weekend, start on a work assignment or just get away. Where they got to, still unknown. An exhaustive search -- covering a mind-boggling 2.97 million square miles, which is nearly the size of the continental United States -- has yielded NEW: Planes depart Australia to resume their search for airplane debris.\nNEW: Official: Passengers' relatives are moved to a different Kuala Lumpur hotel.\nObjects seen on satellite spark intensive search in southern Indian Ocean.\nU.S. officials: Fil NEW: Australian military planes resume their search for two objects detected by satellite over 1,400 miles off Australia's west coast .\nThe search has covered a mind-boggling 2.97 million square miles, which is nearly the size of the continental Uni
1 Washington (CNN) -- Moments after the U.S. Supreme Court ruled on a pair of same-sex marriage cases, the handful of Democrats considering running for president in 2016 stumbled over themselves in a rush of celebratory reaction, blasting out a salvo of congratulatory press releases and tweets. Rulings hailed as historic victory. Voting 5-4 in each of two decisions, the justices struck down part of the Defense of Marriage Act that denied federal benefits to same-sex couples and cleared the way fo Democrats who support same-sex marriage are now playing on increasingly friendly political turf.\nGOP leaders express dismay at ruling, but seem eager to be rid of a polarizing issue.\nThe political fight over marriage is felt most acutely, perhaps, i Hillary Clinton, whose husband signed DOMA into law in 1996, applauded the decision .\nMaryland Gov. Martin O'Malley called the rulings "a powerful step forward" New York Gov. Andrew Cuomo said the opinions were "groundbreaking"
{% endraw %}

Inference

{% raw %}
learn.export(fname='summarize_export.pkl')
{% endraw %} {% raw %}
inf_learn = load_learner(fname='summarize_export.pkl')
inf_learn.blurr_summarize(test_article)
[" Police: About 10 men with pistols and machine guns raid a casino in Switzerland and make off with several hundred thousand Swiss francs .\nThe men, dressed in black clothes and black ski masks, split into two groups during the raid on the Grand Casino Basel .\nOne group tried to break into the casino's vault on the lower level but could not get in . \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0The second group entered the upper level where the roulette and blackjack tables are located and robbed the cashier there . \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0 \xa0There were no serious injuries, although one guest on the Casino floor was kicked in the head by one of the robbers when he moved ."]
{% endraw %}

Tests

The tests below to ensure the core training code above works for all pretrained summarization models available in huggingface. These tests are excluded from the CI workflow because of how long they would take to run and the amount of data that would be required to download.

Note: Feel free to modify the code below to test whatever pretrained summarization models you are working with ... and if any of your pretrained summarization models fail, please submit a github issue (or a PR if you'd like to fix it yourself)

{% raw %}
try: del learn; torch.cuda.empty_cache()
except: pass
{% endraw %} {% raw %}
BLURR_MODEL_HELPER.get_models(task='ConditionalGeneration')
[transformers.modeling_bart.BartForConditionalGeneration,
 transformers.modeling_blenderbot.BlenderbotForConditionalGeneration,
 transformers.modeling_fsmt.FSMTForConditionalGeneration,
 transformers.modeling_mbart.MBartForConditionalGeneration,
 transformers.modeling_pegasus.PegasusForConditionalGeneration,
 transformers.modeling_prophetnet.ProphetNetForConditionalGeneration,
 transformers.modeling_t5.T5ForConditionalGeneration,
 transformers.modeling_xlm_prophetnet.XLMProphetNetForConditionalGeneration]
{% endraw %} {% raw %}
pretrained_model_names = [
    ('facebook/bart-large-cnn',BartForConditionalGeneration),
    ('t5-small', T5ForConditionalGeneration),
    #('google/pegasus-cnn_dailymail', PegasusForConditionalGeneration), ... don't fit on my 1080TI :(
]
{% endraw %} {% raw %}
path = Path('./')
cnndm_df = pd.read_csv(path/'cnndm_sample.csv')
{% endraw %} {% raw %}
#hide_output
bsz = 2
inp_seq_sz = 128; trg_seq_sz = 130

test_results = []
for model_name, model_cls in pretrained_model_names:
    error=None
    
    print(f'=== {model_name} ===\n')
    
    hf_arch, hf_config, hf_tokenizer, hf_model = BLURR_MODEL_HELPER.get_hf_objects(model_name, 
                                                                                   model_cls=model_cls)
    
    print(f'architecture:\t{hf_arch}\ntokenizer:\t{type(hf_tokenizer).__name__}\nmodel:\t\t{type(hf_model).__name__}\n')
    
    # 1. build your DataBlock
    def add_t5_prefix(inp): return f'summarize: {inp}' if (hf_arch == 't5') else inp
    
    hf_batch_tfm = HF_SummarizationBatchTransform(hf_arch, hf_tokenizer, max_length=[inp_seq_sz, trg_seq_sz])
    blocks = (HF_TextBlock(hf_batch_tfm=hf_batch_tfm), noop)
    dblock = DataBlock(blocks=blocks, 
                       get_x=Pipeline([ColReader('article'), add_t5_prefix]), 
                       get_y=ColReader('highlights'), 
                       splitter=RandomSplitter())

    dls = dblock.dataloaders(cnndm_df, bs=bsz)

    # 2. build your Learner
    text_gen_kwargs = {}
    if (hf_arch in ['bart', 't5']):
        text_gen_kwargs = { 
            **hf_config.task_specific_params['summarization'], 
            **{'max_length': 30, 'min_length': 10} 
        }
    
    model = HF_BaseModelWrapper(hf_model)
    model_cb = HF_SummarizationModelCallback(text_gen_kwargs=text_gen_kwargs)

    learn = Learner(dls, 
                    model,
                    opt_func=ranger,
                    loss_func=HF_MaskedLMLoss(),
                    cbs=[model_cb],
                    splitter=partial(summarization_splitter, arch=hf_arch))#.to_fp16()

    learn.create_opt() 
    learn.freeze()
    
    # 3. Run your tests
    b = dls.one_batch()

    try:
        print('*** TESTING DataLoaders ***\n')
        test_eq(len(b), 2)
        test_eq(len(b[0]['input_ids']), bsz)
        test_eq(b[0]['input_ids'].shape, torch.Size([bsz, inp_seq_sz]))
        test_eq(len(b[1]), bsz)

        print('*** TESTING One pass through the model ***')
        preds = learn.model(b[0])
        test_eq(preds[1].shape[0], bsz)
        test_eq(preds[1].shape[2], hf_config.vocab_size)

        print('*** TESTING Training/Results ***')
        learn.fit_one_cycle(1, lr_max=1e-3)

        test_results.append((hf_arch, type(hf_tokenizer).__name__, type(hf_model).__name__, 'PASSED', ''))
        learn.show_results(learner=learn, max_n=2, input_trunc_at=500, target_trunc_at=250)
    except Exception as err:
        test_results.append((hf_arch, type(hf_tokenizer).__name__, type(hf_model).__name__, 'FAILED', err))
    finally:
        # cleanup
        del learn; torch.cuda.empty_cache()
{% endraw %} {% raw %}
arch tokenizer model_name result error
0 bart BartTokenizer BartForConditionalGeneration PASSED
1 t5 T5Tokenizer T5ForConditionalGeneration PASSED
{% endraw %}

Cleanup