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[Longformer] fix model name in examples (#4653)

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* fix longformer model names in examples

* a better name for the notebook
This commit is contained in:
Iz Beltagy
2020-05-29 04:12:35 -07:00
committed by GitHub
parent b5015a2a0f
commit 91487cbb8e
2 changed files with 11 additions and 11 deletions
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20
src/transformers/modeling_longformer.py
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@@ -572,8 +572,8 @@ class LongformerModel(RobertaModel):
import torch
from transformers import LongformerModel, LongformerTokenizer
model = LongformerModel.from_pretrained('longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
model = LongformerModel.from_pretrained('allenai/longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000) # long input document
input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0) # batch of size 1
@@ -681,8 +681,8 @@ class LongformerForMaskedLM(BertPreTrainedModel):
import torch
from transformers import LongformerForMaskedLM, LongformerTokenizer
model = LongformerForMaskedLM.from_pretrained('longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
model = LongformerForMaskedLM.from_pretrained('allenai/longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000) # long input document
input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0) # batch of size 1
@@ -769,8 +769,8 @@ class LongformerForSequenceClassification(BertPreTrainedModel):
from transformers import LongformerTokenizer, LongformerForSequenceClassification
import torch
tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
model = LongformerForSequenceClassification.from_pretrained('longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
model = LongformerForSequenceClassification.from_pretrained('allenai/longformer-base-4096')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
@@ -909,8 +909,8 @@ class LongformerForQuestionAnswering(BertPreTrainedModel):
from transformers import LongformerTokenizer, LongformerForQuestionAnswering
import torch
tokenizer = LongformerTokenizer.from_pretrained("longformer-large-4096-finetuned-triviaqa")
model = LongformerForQuestionAnswering.from_pretrained("longformer-large-4096-finetuned-triviaqa")
tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
model = LongformerForQuestionAnswering.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
encoding = tokenizer.encode_plus(question, text, return_tensors="pt")
@@ -1031,8 +1031,8 @@ class LongformerForTokenClassification(BertPreTrainedModel):
from transformers import LongformerTokenizer, LongformerForTokenClassification
import torch
tokenizer = LongformerTokenizer.from_pretrained('longformer-base-4096')
model = LongformerForTokenClassification.from_pretrained('longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
model = LongformerForTokenClassification.from_pretrained('allenai/longformer-base-4096')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)