[doc] Fix broken links + remove crazy big notebook

model_cards/mrm8488/GPT-2-finetuned-CORD19/README.md

@@ -40,7 +40,7 @@ python run_language_modeling.py \
 
 ## Model in action / Example of usage ✒
 
-You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py)
+You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py)
 
 ```bash
 python run_generation.py \

model_cards/mrm8488/GPT-2-finetuned-covid-bio-medrxiv/README.md

@@ -37,7 +37,7 @@ python run_language_modeling.py \
 
 ## Model in action / Example of usage: ✒
 
-You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py)
+You can get the following script [here](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py)
 
 ```bash
 python run_generation.py \

model_cards/mrm8488/TinyBERT-spanish-uncased-finetuned-ner/README.md

@@ -19,7 +19,7 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
 | Dev                    | 2.2 K |
 
 
-- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py)
+- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
 
 - Labels covered:
 

model_cards/mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md

@@ -29,7 +29,7 @@ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following com
 
 ```bash
 export SQUAD_DIR=path/to/nl_squad
-python transformers/examples/run_squad.py \
+python transformers/examples/question-answering/run_squad.py \
   --model_type bert \
   --model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
   --do_train \

model_cards/mrm8488/bert-medium-finetuned-squadv2/README.md

@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
 ## Model training
 
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)
+The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
 
 ## Results:
 

model_cards/mrm8488/bert-mini-finetuned-squadv2/README.md

@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
 ## Model training
 
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)
+The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
 
 ## Results:
 

model_cards/mrm8488/bert-small-finetuned-squadv2/README.md

@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
 ## Model training
 
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)
+The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
 
 ## Results:
 

model_cards/mrm8488/bert-small-finetuned-typo-detection/README.md

@@ -11,7 +11,7 @@ thumbnail:
 
 - Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚
 
-- [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) 🏋️‍♂️
+- [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py) 🏋️‍♂️
 
 ## Metrics on test set 📋
 

model_cards/mrm8488/bert-spanish-cased-finetuned-ner/README.md

@@ -19,7 +19,7 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
 | Dev                    | 2.2 K |
 
 
-- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py)
+- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
 
 - Labels covered:
 

model_cards/mrm8488/bert-spanish-cased-finetuned-pos-syntax/README.md

@@ -11,7 +11,7 @@ This model is a fine-tuned version of the Spanish BERT [(BETO)](https://github.c
 
 - [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
 
-#### [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py)
+#### [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
 
 #### 21 Syntax annotations (Labels) covered:
 

model_cards/mrm8488/bert-spanish-cased-finetuned-pos/README.md

@@ -19,7 +19,7 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
 | Dev                    | 50 K |
 
 
-- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py)
+- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)
 
 - **60** Labels covered:
 

model_cards/mrm8488/bert-tiny-finetuned-squadv2/README.md

@@ -29,7 +29,7 @@ The smaller BERT models are intended for environments with restricted computatio
 ## Model training
 
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)
+The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
 
 ## Results:
 

model_cards/mrm8488/distilbert-base-multi-cased-finetuned-typo-detection/README.md

@@ -11,7 +11,7 @@ thumbnail:
 
 - Dataset: [GitHub Typo Corpus](https://github.com/mhagiwara/github-typo-corpus) 📚 for 15 languages
 
-- [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) 🏋️‍♂️
+- [Fine-tune script on NER dataset provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py) 🏋️‍♂️
 
 ## Metrics on test set 📋
 

model_cards/mrm8488/distilbert-multi-finetuned-for-xqua-on-tydiqa/README.md

@@ -31,7 +31,7 @@ The model was fine-tuned on a Tesla P100 GPU and 25GB of RAM.
 The script is the following:
 
 ```python
-python transformers/examples/run_squad.py \
+python transformers/examples/question-answering/run_squad.py \
   --model_type distilbert \
   --model_name_or_path distilbert-base-multilingual-cased \
   --do_train \

model_cards/mrm8488/spanbert-finetuned-squadv1/README.md

@@ -26,7 +26,7 @@ thumbnail:
 ## Model training
 
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)
+The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
 
 ## Results:
 

model_cards/mrm8488/spanbert-finetuned-squadv2/README.md

@@ -23,7 +23,7 @@ thumbnail:
 ## Model training
 
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)
+The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
 
 ## Results:
 

src/transformers/modeling_albert.py

@@ -932,7 +932,7 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
     Examples::
 
         # The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the
-        # examples/run_squad.py example to see how to fine-tune a model to a question answering task.
+        # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
 
         from transformers import AlbertTokenizer, AlbertForQuestionAnswering
         import torch

src/transformers/modeling_roberta.py

@@ -643,7 +643,7 @@ class RobertaForQuestionAnswering(BertPreTrainedModel):
     Examples::
 
         # The checkpoint roberta-large is not fine-tuned for question answering. Please see the
-        # examples/run_squad.py example to see how to fine-tune a model to a question answering task.
+        # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
 
         from transformers import RobertaTokenizer, RobertaForQuestionAnswering
         import torch

src/transformers/modeling_tf_albert.py

@@ -865,7 +865,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel):
     Examples::
 
         # The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the
-        # examples/run_squad.py example to see how to fine-tune a model to a question answering task.
+        # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
 
         import tensorflow as tf
         from transformers import AlbertTokenizer, TFAlbertForQuestionAnswering

src/transformers/modeling_tf_roberta.py

@@ -481,7 +481,7 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel):
     Examples::
 
         # The checkpoint roberta-base is not fine-tuned for question answering. Please see the
-        # examples/run_squad.py example to see how to fine-tune a model to a question answering task.
+        # examples/question-answering/run_squad.py example to see how to fine-tune a model to a question answering task.
 
         import tensorflow as tf
         from transformers import RobertaTokenizer, TFRobertaForQuestionAnswering