[Flax] Add links to google colabs (#12146)

* fix_torch_device_generate_test

* remove @

* add colab links

examples/flax/README.md

@@ -58,5 +58,6 @@ The following table lists all of our examples on how to use 🤗 Transformers wi
 
 | Task | Example model | Example dataset | 🤗 Datasets | Colab
 |---|---|---|:---:|:---:|
-| [**`masked-language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) | BERT | OSCAR | ✅ | [![Open In Colab (TODO: Patrick)](https://colab.research.google.com/assets/colab-badge.svg)]()
+| [**`causal-language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) | GPT2 | OSCAR | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/causal_language_modeling_flax.ipynb)
+| [**`masked-language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling) | RoBERTa | OSCAR | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/masked_language_modeling_flax.ipynb)
 | [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) | BERT | GLUE | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification_flax.ipynb)

examples/flax/language-modeling/README.md

@@ -98,23 +98,23 @@ Next we can run the example script to pretrain the model:
 
 ```bash
 ./run_mlm_flax.py \
-        --output_dir="./runs" \
-        --model_type="roberta" \
-        --config_name="${MODEL_DIR}" \
-        --tokenizer_name="${MODEL_DIR}" \
-        --dataset_name="oscar" \
-        --dataset_config_name="unshuffled_deduplicated_no" \
-        --max_seq_length="128" \
-        --weight_decay="0.01" \
-        --per_device_train_batch_size="128" \
-        --per_device_eval_batch_size="128" \
-        --learning_rate="3e-4" \
-        --warmup_steps="1000" \
-        --overwrite_output_dir \
-        --pad_to_max_length \
-        --num_train_epochs="18" \
-        --adam_beta1="0.9" \
-        --adam_beta2="0.98"
+    --output_dir="./runs" \
+    --model_type="roberta" \
+    --config_name="${MODEL_DIR}" \
+    --tokenizer_name="${MODEL_DIR}" \
+    --dataset_name="oscar" \
+    --dataset_config_name="unshuffled_deduplicated_no" \
+    --max_seq_length="128" \
+    --weight_decay="0.01" \
+    --per_device_train_batch_size="128" \
+    --per_device_eval_batch_size="128" \
+    --learning_rate="3e-4" \
+    --warmup_steps="1000" \
+    --overwrite_output_dir \
+    --pad_to_max_length \
+    --num_train_epochs="18" \
+    --adam_beta1="0.9" \
+    --adam_beta2="0.98"
 ```
 
 Training should converge at a loss and accuracy 
@@ -235,27 +235,27 @@ mkdir -p ${MODEL_DIR}
 
 ```bash
 python3 xla_spawn.py --num_cores ${NUM_TPUS} run_mlm.py --output_dir="./runs" \
-										--model_type="roberta" \
-										--config_name="${MODEL_DIR}" \
-										--tokenizer_name="${MODEL_DIR}" \
-										--dataset_name="oscar" \
-										--dataset_config_name="unshuffled_deduplicated_no" \
-										--max_seq_length="128" \
-										--weight_decay="0.01" \
-										--per_device_train_batch_size="128" \
-										--per_device_eval_batch_size="128" \
-										--learning_rate="3e-4" \
-										--warmup_steps="1000" \
-										--overwrite_output_dir \
-										--num_train_epochs="18" \
-										--adam_beta1="0.9" \
-										--adam_beta2="0.98" \
-										--do_train \
-										--do_eval \
-										--logging_steps="500" \
-										--evaluation_strategy="epoch" \
-										--report_to="tensorboard" \
-										--save_strategy="no"
+    --model_type="roberta" \
+    --config_name="${MODEL_DIR}" \
+    --tokenizer_name="${MODEL_DIR}" \
+    --dataset_name="oscar" \
+    --dataset_config_name="unshuffled_deduplicated_no" \
+    --max_seq_length="128" \
+    --weight_decay="0.01" \
+    --per_device_train_batch_size="128" \
+    --per_device_eval_batch_size="128" \
+    --learning_rate="3e-4" \
+    --warmup_steps="1000" \
+    --overwrite_output_dir \
+    --num_train_epochs="18" \
+    --adam_beta1="0.9" \
+    --adam_beta2="0.98" \
+    --do_train \
+    --do_eval \
+    --logging_steps="500" \
+    --evaluation_strategy="epoch" \
+    --report_to="tensorboard" \
+    --save_strategy="no"
 ```
 
 ### Script to compare pre-training with PyTorch on 8 GPU V100's
@@ -281,27 +281,27 @@ mkdir -p ${MODEL_DIR}
 
 ```bash
 python3 -m torch.distributed.launch --nproc_per_node ${NUM_GPUS} run_mlm.py \
-                        --output_dir="./runs" \
-                        --model_type="roberta" \
-                        --config_name="${MODEL_DIR}" \
-                        --tokenizer_name="${MODEL_DIR}" \
-                        --dataset_name="oscar" \
-                        --dataset_config_name="unshuffled_deduplicated_no" \
-                        --max_seq_length="128" \
-                        --weight_decay="0.01" \
-                        --per_device_train_batch_size="32" \
-                        --per_device_eval_batch_size="32" \
-                        --gradient_accumulation="4" \
-                        --learning_rate="3e-4" \
-                        --warmup_steps="1000" \
-                        --overwrite_output_dir \
-                        --num_train_epochs="18" \
-                        --adam_beta1="0.9" \
-                        --adam_beta2="0.98" \
-                        --do_train \
-                        --do_eval \
-                        --logging_steps="500" \
-                        --evaluation_strategy="steps" \
-                        --report_to="tensorboard" \
-                        --save_strategy="no"
+    --output_dir="./runs" \
+    --model_type="roberta" \
+    --config_name="${MODEL_DIR}" \
+    --tokenizer_name="${MODEL_DIR}" \
+    --dataset_name="oscar" \
+    --dataset_config_name="unshuffled_deduplicated_no" \
+    --max_seq_length="128" \
+    --weight_decay="0.01" \
+    --per_device_train_batch_size="32" \
+    --per_device_eval_batch_size="32" \
+    --gradient_accumulation="4" \
+    --learning_rate="3e-4" \
+    --warmup_steps="1000" \
+    --overwrite_output_dir \
+    --num_train_epochs="18" \
+    --adam_beta1="0.9" \
+    --adam_beta2="0.98" \
+    --do_train \
+    --do_eval \
+    --logging_steps="500" \
+    --evaluation_strategy="steps" \
+    --report_to="tensorboard" \
+    --save_strategy="no"
 ```