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cleaning up example docstrings

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thomwolf
2019-07-27 20:25:39 +02:00
parent 4cc1bf81ee
commit bfbe52ec39
15 changed files with 509 additions and 509 deletions
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pytorch_transformers/modeling_gpt2.py
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@@ -433,12 +433,12 @@ class GPT2Model(GPT2PreTrainedModel):
Examples::
>>> config = GPT2Config.from_pretrained('gpt2')
>>> tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
>>> model = GPT2Model(config)
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids)
>>> last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
config = GPT2Config.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model(config)
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config):
@@ -567,12 +567,12 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
Examples::
>>> config = GPT2Config.from_pretrained('gpt2')
>>> tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
>>> model = GPT2LMHeadModel(config)
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids, labels=input_ids)
>>> loss, logits = outputs[:2]
config = GPT2Config.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel(config)
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=input_ids)
loss, logits = outputs[:2]
"""
def __init__(self, config):
@@ -683,14 +683,14 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
Examples::
>>> config = GPT2Config.from_pretrained('gpt2')
>>> tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
>>> model = GPT2DoubleHeadsModel(config)
>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] # Assume you've added [CLS] to the vocabulary
>>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
>>> mc_token_ids = torch.tensor([-1, -1]).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids, mc_token_ids)
>>> lm_prediction_scores, mc_prediction_scores = outputs[:2]
config = GPT2Config.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2DoubleHeadsModel(config)
choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] # Assume you've added [CLS] to the vocabulary
input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
mc_token_ids = torch.tensor([-1, -1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, mc_token_ids)
lm_prediction_scores, mc_prediction_scores = outputs[:2]
"""
def __init__(self, config):