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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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10
tests/trainer/test_trainer.py
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@@ -1537,7 +1537,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
with tempfile.TemporaryDirectory() as tmpdir:
testargs = f"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--model_name_or_path distilbert/distilbert-base-uncased
--task_name mrpc
--do_train
--do_eval
@@ -1886,7 +1886,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
@slow
def test_trainer_eval_mrpc(self):
MODEL_ID = "bert-base-cased-finetuned-mrpc"
MODEL_ID = "google-bert/bert-base-cased-finetuned-mrpc"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
data_args = GlueDataTrainingArguments(
@@ -1901,7 +1901,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
@slow
def test_trainer_eval_multiple(self):
MODEL_ID = "gpt2"
MODEL_ID = "openai-community/gpt2"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
dataset = LineByLineTextDataset(
@@ -1930,7 +1930,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
@slow
def test_trainer_eval_lm(self):
MODEL_ID = "distilroberta-base"
MODEL_ID = "distilbert/distilroberta-base"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
dataset = LineByLineTextDataset(
tokenizer=tokenizer,
@@ -2384,7 +2384,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
"launch",
script_path,
"--model_name_or_path",
"t5-small",
"google-t5/t5-small",
"--per_device_train_batch_size",
"1",
"--output_dir",

8
tests/trainer/test_trainer_seq2seq.py
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@@ -35,7 +35,7 @@ class Seq2seqTrainerTester(TestCasePlus):
@require_torch
def test_finetune_bert2bert(self):
bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny", "prajjwal1/bert-tiny")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
bert2bert.config.vocab_size = bert2bert.config.encoder.vocab_size
bert2bert.config.eos_token_id = tokenizer.sep_token_id
@@ -144,11 +144,11 @@ class Seq2seqTrainerTester(TestCasePlus):
MAX_TARGET_LENGTH = 256
dataset = datasets.load_dataset("gsm8k", "main", split="train[:38]")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors="pt", padding="longest")
gen_config = GenerationConfig.from_pretrained(
"t5-small", max_length=None, min_length=None, max_new_tokens=256, min_new_tokens=1, num_beams=5
"google-t5/t5-small", max_length=None, min_length=None, max_new_tokens=256, min_new_tokens=1, num_beams=5
)
training_args = Seq2SeqTrainingArguments(".", predict_with_generate=True)