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Tokenizers should be framework agnostic (#8599)

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* Tokenizers should be framework agnostic

* Run the slow tests

* Not testing

* Fix documentation

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Lysandre Debut
2020-11-17 14:03:03 -05:00
committed by GitHub
parent 7f3b41a306
commit 3095ee9dab
28 changed files with 73 additions and 177 deletions
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4
docs/source/model_doc/marian.rst
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@@ -78,7 +78,7 @@ require 3 character language codes:
tokenizer = MarianTokenizer.from_pretrained(model_name)
print(tokenizer.supported_language_codes)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text))
translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt"))
tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
# ["c'est une phrase en anglais que nous voulons traduire en français",
# 'Isto deve ir para o português.',
@@ -150,7 +150,7 @@ Example of translating english to many romance languages, using old-style 2 char
print(tokenizer.supported_language_codes)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text))
translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt"))
tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
# ["c'est une phrase en anglais que nous voulons traduire en français", 'Isto deve ir para o português.', 'Y esto al español']

4
docs/source/model_doc/mbart.rst
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@@ -44,7 +44,7 @@ the sequences for sequence-to-sequence fine-tuning.
example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian, return_tensors="pt")
model(input_ids=batch['input_ids'], labels=batch['labels']) # forward pass
- Generation
@@ -58,7 +58,7 @@ the sequences for sequence-to-sequence fine-tuning.
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
article = "UN Chief Says There Is No Military Solution in Syria"
batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX", return_tensors="pt")
translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"

2
docs/source/model_doc/pegasus.rst
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@@ -78,7 +78,7 @@ Usage Example
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors="pt").to(torch_device)
translated = model.generate(**batch)
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers."