--- 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
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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
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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
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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)
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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())
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dls = dblock.dataloaders(cnndm_df, bs=2)
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b = dls.one_batch()
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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 Dan Condon believes in recycling. Just not when it comes to his hotel towels. Condon composts when he's at home in Boulder, Colorado. He eats local, organic and fair-trade food and drives a Honda CR-Z hybrid sports car. You might call him green. Except he's not so green when he travels for his work at an education nonprofit and stays in a hotel, which happens about 10 weeks per year. There, he uses a new towel every day. And don't try to bribe him with a drink or dessert coupon to get him to reuse the same one. "I could care less about rewards for environmentally conscious behavior unless it's miles," Condon wrote in an e-mail. If hotels can't convince a hybrid-driving recycling enthusiast like Condon to go green while traveling, how can they possibly convince everyone else? 9 glamorous movie-star hotels. That's the problem of hotels trying to "green" your hotel stay. After guests have paid a pretty penny for a night at the inn, even the most environmental guests may want to treat themselves to fresh towels every day and those little bottles of sweet-smelling shampoo. Despite the fact that most people describe themselves in surveys as environmentally conscious and as preferring green products, Hotel guests who "go green" are happier with their stay.\nIncreasing water and energy costs are pushing hotels to cut costs wherever they can.\nMany hotels find that guests don't mind using the same towels and sheets every night.\nTripAdvisor will be adding a green label for hotels listed on its site.
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Metrics

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

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calculate_rouge[source]

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

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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

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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

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

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summarization_splitter[source]

summarization_splitter(m, arch)

Custom param splitter for summarization models

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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.

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class HF_MaskedLMLoss[source]

HF_MaskedLMLoss()

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text_gen_kwargs = { **hf_config.task_specific_params['summarization'], **{'max_length': 130, 'min_length': 30} }
text_gen_kwargs
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-10-aa663c016f49> in <module>
----> 1 text_gen_kwargs = { **hf_config.task_specific_params['summarization'], **{'max_length': 130, 'min_length': 30} }
      2 text_gen_kwargs

NameError: name 'hf_config' is not defined
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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()
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{% endraw %} {% raw %}
b = dls.one_batch()
preds = learn.model(b[0])
len(preds),preds[0], preds[1].shape
(3,
 tensor(3.7947, 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
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learn.lr_find(suggestions=True)
SuggestedLRs(lr_min=0.0002511886414140463, lr_steep=0.0003981071640737355)
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learn.fit_one_cycle(1, lr_max=4e-5)
epoch train_loss valid_loss rouge1 rouge2 rougeL time
0 1.888739 1.750462 0.389055 0.165875 0.264984 03:53
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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 raided a casino in Switzerland and made off
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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 ===
 Paralympic movement was born in Stoke Mandeville, outside London, in 1948.
2012 Games will be the biggest yet, with 4,200 competitors from 165 countries.
In an echo of the first, post-World War II Games, injured veterans are among the athletes.
They include a U.S. naval officer blinded in Afghanistan and a Briton who lost an arm in Iraq.

=== Prediction ===
 "Stoke Mandeville Games" were organized in 1948 to coincide with the London Olympics, the second to be held in Britain.
In 1948, 14 men and two women took part in a wheelchair archery contest at the hospital in Buckinghamshire.
A year later, six teams competed in wheelchair netball, a forerunner of wheelchair basketball.
The first Paralympic Games were held in 1956, and were the first to take place outside of Britain.
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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.

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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)

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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 ===
 10 men with pistols and machine guns rob Swiss casino in early hours of Sunday morning .
They make off with several hundred thousand Swiss francs from Grand Casino Basel .
A woman driving by unknowingly blocks the robbers' vehicles and is beaten by one of them .
There were about 600 people in the casino at the time of the robbery .
The robbers spoke French and drove vehicles with French license plates, police say .

=== Prediction 2 ===
 10 men with pistols and machine guns rob Swiss casino in early hours of Sunday morning .
They make off with several hundred thousand Swiss francs from Grand Casino Basel .
A woman driving by unknowingly blocks the robbers' vehicles and is beaten by one of them .
There were about 600 people in the casino at the time of the robbery .
The robbers spoke French and drove vehicles with French license plates .

=== Prediction 3 ===
 10 men with pistols and machine guns rob Swiss casino in early hours of Sunday morning .
They make off with several hundred thousand Swiss francs from Grand Casino Basel .
A woman driving by unknowingly blocks the robbers' vehicles and is beaten by one of them .
There were about 600 people in the casino at the time of the robbery .
No serious injuries were reported .

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

{% raw %}
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learn.show_results(learner=learn)
text target prediction
0 London (CNN) -- In 1948, a hospital outside London witnessed the birth of the Paralympic movement, as a Jewish doctor who had fled Nazi Germany sought to change the lives of patients with spinal injuries -- and inspire new hope in them through sport. The first "Stoke Mandeville Games" were organized in 1948 to coincide with the London Olympics, the second to be held in Britain. Named for the hospital in Buckinghamshire where Prof. Ludwig Guttmann's pioneering spinal injuries unit was based, the competitors in those initial Games -- 14 men and two women -- took part in a wheelchair archery contest. Many were military veterans injured on the battlefields of World War II. Just a year later, six teams competed at Stoke Mandeville -- with wheelchair netball, a forerunner of wheelchair basketball, being introduced -- as sport became a central part of a rehabilitation process that had been revolutionized by Guttmann. In 1956, a "statement of intent" was unveiled for the Games, which were by this time international, according to to the Mandeville Legacy website run by the local authority. It read: "The aim of the Stoke Mandeville Games is to unite paralyzed men and women from all parts of the world in an international sports Paralympic movement was born in Stoke Mandeville, outside London, in 1948.\n2012 Games will be the biggest yet, with 4,200 competitors from 165 countries.\nIn an echo of the first, post-World War II Games, injured veterans are among the athletes.\nThey include a U.S. naval officer blinded in Afghanistan and a Briton who lost an arm in Iraq. "Stoke Mandeville Games" were organized in 1948 to coincide with the London Olympics, the second to be held in Britain .\nIn 1948, 14 men and two women took part in a wheelchair archery contest at the hospital in Buckinghamshire .\nA year later, six teams competed in wheelchair netball, a forerunner of wheelchair basketball .\nThe first Paralympic Games were held in 1956, and were the first to take place outside of Britain .
1 (CNN) -- To Disney or not to Disney? For many travelers, especially those with children, it's not even a question they ask. They already know the answer. "Yes." To these visitors, Disney is Mickey Mouse, princesses, magic and fun. It's happy memories of childhood brought back to life in your children, a clean place where the rides are safe and the Disney characters are always happy to pose for pictures with your kids. That's Deb Koma, who visited once as a child and walked back into the Magic Kingdom in the mid-1990s with her young son. "It was so perfect, everybody was so happy, everything was so maintained," said Koma, who now works for the AllEars.net, an unofficial Disney planning and fan site. "You were in a perfect fantasy world. That, and my little boy loved it." But for other vacationers, Disney inspires a firm "no." To those travelers, Disney is merely a commercial machine built to sell tickets, overpriced toys and a stereotype of girls as princesses. They may remember visiting Disneyland or Disney World when they were children, but they aren't taking their kids there. Downtown Disney to make way for Disney Springs. That's the Rev. Disney represents magical stories and fun family to fans.\nSome parents delight in their children's wonder during a first visit to Disney.\nSome critics think the company encourages kids to buy too much stuff.\nOthers worry the princess culture limits girls' potential. To some visitors, Disney is Mickey Mouse, princesses, magic and fun .\nBut for other vacationers, Disney inspires a firm "no"\nThey may remember visiting Disneyland or Disney World when they were children, but they aren't taking their kids there .\nThey don't like the idea of overpriced toys and a stereotype of girls as princesses .
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Inference

{% raw %}
learn.export(fname='summarize_export.pkl')
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inf_learn = load_learner(fname='summarize_export.pkl')
inf_learn.blurr_summarize(test_article)
[" 10 men with pistols and machine guns rob Swiss casino in early hours of Sunday morning .\nThey make off with several hundred thousand Swiss francs from Grand Casino Basel .\nA woman driving by unknowingly blocks the robbers' vehicles and is beaten by one of them .\nThere were about 600 people in the casino at the time of the robbery .\nThe robbers spoke French and drove vehicles with French license plates, police say ."]
{% 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)
    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
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Cleanup