[doc] Fix broken links + remove crazy big notebook

README.md

@@ -414,7 +414,7 @@ Training with these hyper-parameters gave us the following results:
 This example code fine-tunes BERT on the SQuAD dataset using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
+python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
     --model_type bert \
     --model_name_or_path bert-large-uncased-whole-word-masking \
     --do_train \
@@ -447,7 +447,7 @@ The generation script includes the [tricks](https://github.com/rusiaaman/XLNet-g
 Here is how to run the script with the small version of OpenAI GPT-2 model:
 
 ```shell
-python ./examples/run_generation.py \
+python ./examples/text-generation/run_generation.py \
     --model_type=gpt2 \
     --length=20 \
     --model_name_or_path=gpt2 \
@@ -455,7 +455,7 @@ python ./examples/run_generation.py \
 
 and from the Salesforce CTRL model:
 ```shell
-python ./examples/run_generation.py \
+python ./examples/text-generation/run_generation.py \
     --model_type=ctrl \
     --length=20 \
     --model_name_or_path=ctrl \

docs/source/bertology.rst

@@ -15,4 +15,4 @@ In order to help this new field develop, we have included a few additional featu
 * accessing all the attention weights for each head of BERT/GPT/GPT-2,
 * retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
 
-To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
+To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/bertology/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.

docs/source/examples.md

@@ -29,7 +29,7 @@ pip install -r ./examples/requirements.txt
 
 ## TensorFlow 2.0 Bert models on GLUE
 
-Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_tf_glue.py).
+Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py).
 
 Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the  MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).
 
@@ -93,7 +93,7 @@ python run_glue_tpu.py \
 
 ## Language model training
 
-Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py).
+Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).
 
 Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT
 to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa
@@ -155,7 +155,7 @@ python run_language_modeling.py \
 
 ## Language generation
 
-Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py).
+Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py).
 
 Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
 A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
@@ -364,7 +364,7 @@ Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
 ```bash
 #training on 4 tesla V100(16GB) GPUS
 export SWAG_DIR=/path/to/swag_data_dir
-python ./examples/run_multiple_choice.py \
+python ./examples/multiple-choice/run_multiple_choice.py \
 --task_name swag \
 --model_name_or_path roberta-base \
 --do_train \
@@ -388,7 +388,7 @@ eval_loss = 0.44457291918821606
 
 ## SQuAD
 
-Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py).
+Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py).
 
 #### Fine-tuning BERT on SQuAD1.0
 
@@ -437,7 +437,7 @@ exact_match = 81.22
 Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
+python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
     --model_type bert \
     --model_name_or_path bert-large-uncased-whole-word-masking \
     --do_train \
@@ -548,7 +548,7 @@ Larger batch size may improve the performance while costing more memory.
 
 ## XNLI
 
-Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py).
+Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
 
 [XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
 

docs/source/main_classes/processors.rst

@@ -74,7 +74,7 @@ This library hosts the processor to load the XNLI data:
 Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
 
 An example using these processors is given in the
-`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_xnli.py>`__ script.
+`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/text-classification/run_xnli.py>`__ script.
 
 
 SQuAD
@@ -150,4 +150,4 @@ Example::
 
 
 Another example using these processors is given in the
-`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/run_squad.py>`__ script.
+`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py>`__ script.

docs/source/model_doc/xlnet.rst

@@ -29,7 +29,7 @@ Tips:
   XLNet is pretrained using only a sub-set of the output tokens as target which are selected
   with the `target_mapping` input.
 - To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
-  `target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)
+  `target_mapping` inputs to control the attention span and outputs (see examples in `examples/text-generation/run_generation.py`)
 - XLNet is one of the few models that has no sequence length limit.
 
 The original code can be found `here <https://github.com/zihangdai/xlnet/>`_.

docs/source/multilingual.rst

@@ -80,7 +80,7 @@ You can then feed it all as input to your model:
     outputs = model(input_ids, langs=langs)
 
 
-The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/run_generation.py>`__
+The example `run_generation.py <https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py>`__
 can generate text using the CLM checkpoints from XLM, using the language embeddings.
 
 XLM without Language Embeddings

examples/README.md

@@ -17,7 +17,7 @@ This is still a work-in-progress – in particular documentation is still sparse
 | Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab | One-click Deploy to Azure (wip) | 
 |---|---|:---:|:---:|:---:|:---:|:---:|
 | [`language-modeling`](./language-modeling) | Raw text | ✅ | - | - | - | - |
-| [`text-classification`](./text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/trainer/01_text_classification.ipynb) | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json) |
+| [`text-classification`](./text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb) | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json) |
 | [`token-classification`](./token-classification) | CoNLL NER | ✅ | ✅ | ✅ | - | - |
 | [`multiple-choice`](./multiple-choice) | SWAG, RACE, ARC | ✅ | - | - | - | - |
 

examples/distillation/run_squad_w_distillation.py

@@ -13,7 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" This is the exact same script as `examples/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation."""
+""" This is the exact same script as `examples/question-answering/run_squad.py` (as of 2020, January 8th) with an additional and optional step of distillation."""
 
 import argparse
 import glob

examples/language-modeling/README.md

 
 ## Language model training
 
-Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/run_language_modeling.py).
+Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).
 
 Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT
 to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa

examples/multiple-choice/README.md

@@ -8,7 +8,7 @@ Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
 ```bash
 #training on 4 tesla V100(16GB) GPUS
 export SWAG_DIR=/path/to/swag_data_dir
-python ./examples/run_multiple_choice.py \
+python ./examples/multiple-choice/run_multiple_choice.py \
 --task_name swag \
 --model_name_or_path roberta-base \
 --do_train \

examples/question-answering/README.md

@@ -2,7 +2,7 @@
 
 ## SQuAD
 
-Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py).
+Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py).
 
 #### Fine-tuning BERT on SQuAD1.0
 
@@ -51,7 +51,7 @@ exact_match = 81.22
 Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/run_squad.py \
+python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
     --model_type bert \
     --model_name_or_path bert-large-uncased-whole-word-masking \
     --do_train \

examples/text-classification/README.md

@@ -2,7 +2,7 @@
 
 # Run TensorFlow 2.0 version
 
-Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_tf_glue.py).
+Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py).
 
 Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the  MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).
 
@@ -256,7 +256,7 @@ TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recal
 
 # XNLI
 
-Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py).
+Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
 
 [XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
 

examples/text-generation/README.md

 ## Language generation
 
-Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py).
+Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py).
 
 Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
 A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you

examples/text-generation/pplm/run_pplm.py

@@ -17,10 +17,10 @@
 
 """
 Example command with bag of words:
-python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
+python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
 
 Example command with discriminator:
-python examples/run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95
+python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95
 """
 
 import argparse

model_cards/fmikaelian/camembert-base-fquad/README.md

@@ -11,7 +11,7 @@ A baseline model for question-answering in french ([CamemBERT](https://camembert
 ## Training hyperparameters
 
 ```shell
-python3 ./examples/run_squad.py \
+python3 ./examples/question-answering/run_squad.py \
 --model_type camembert \
 --model_name_or_path camembert-base \
 --do_train \

model_cards/fmikaelian/camembert-base-squad/README.md

@@ -11,7 +11,7 @@ A baseline model for question-answering in french ([CamemBERT](https://camembert
 ## Training hyperparameters
 
 ```shell
-python3 ./examples/run_squad.py \
+python3 ./examples/question-answering/run_squad.py \
 --model_type camembert \
 --model_name_or_path camembert-base \
 --do_train \

model_cards/fmikaelian/flaubert-base-uncased-squad/README.md

@@ -11,7 +11,7 @@ A baseline model for question-answering in french ([flaubert](https://github.com
 ## Training hyperparameters
 
 ```shell
-python3 ./examples/run_squad.py \
+python3 ./examples/question-answering/run_squad.py \
 --model_type flaubert \
 --model_name_or_path flaubert-base-uncased \
 --do_train \

model_cards/ktrapeznikov/albert-xlarge-v2-squad-v2/README.md

 ### Model
-**[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)**
+**[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
 
 ### Training Parameters
 Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb

model_cards/ktrapeznikov/biobert_v1.1_pubmed_squad_v2/README.md

 ### Model
-**[`monologg/biobert_v1.1_pubmed`](https://huggingface.co/monologg/biobert_v1.1_pubmed)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)**
+**[`monologg/biobert_v1.1_pubmed`](https://huggingface.co/monologg/biobert_v1.1_pubmed)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
 
 This model is cased.
 

model_cards/ktrapeznikov/scibert_scivocab_uncased_squad_v2/README.md

 ### Model
-**[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py)**
+**[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
 
 ### Training Parameters
 Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb