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

* fix longformer model names in examples

* a better name for the notebook

notebooks/README.md

@@ -30,5 +30,5 @@ Pull Request so it can be included under the Community notebooks.
 | [Fine-tune BART for Summarization](https://github.com/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb) | How to fine-tune BART for summarization with fastai using blurr | [Wayde Gilliam](https://ohmeow.com/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb) |
 | [Fine-tune a pre-trained Transformer on anyone's tweets](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)  | How to generate tweets in the style of your favorite Twitter account by fine-tune a GPT-2 model |  [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) |
 | [A Step by Step Guide to Tracking Hugging Face Model Performance](https://colab.research.google.com/drive/1NEiqNPhiouu2pPwDAVeFoN4-vTYMz9F8)  | A quick tutorial for training NLP models with HuggingFace and & visualizing their performance with Weights & Biases |  [Jack Morris](https://github.com/jxmorris12) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NEiqNPhiouu2pPwDAVeFoN4-vTYMz9F8) |
-| [Pretrain Longformer](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb)  | How to convert existing pretrained models into their Long version |  [Iz Beltagy](https://beltagy.net) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) |
+| [Pretrain Longformer](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb)  | How to build a "long" version of existing pretrained models |  [Iz Beltagy](https://beltagy.net) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) |
 | [Fine-tune Longformer for QA](https://github.com/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) | How to fine-tune longformer model for QA task | [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) |

src/transformers/modeling_longformer.py

@@ -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)