Add Readme for language modeling scripts with accelerate (huggingface…

…#11073)

examples/language-modeling/README.md

@@ -22,8 +22,7 @@ ALBERT, BERT, DistilBERT, RoBERTa, XLNet... GPT and GPT-2 are trained or fine-tu
 loss. XLNet uses permutation language modeling (PLM), you can find more information about the differences between those
 objectives in our [model summary](https://huggingface.co/transformers/model_summary.html).
 
-These scripts leverage the 🤗 Datasets library and the Trainer API. You can easily customize them to your needs if you
-need extra processing on your datasets.
+There are two sets of scripts provided. The first set leverages the Trainer API. The second set with `no_trainer` in the suffix uses a custom training loop and leverages the 🤗 Accelerate library . Both sets use the 🤗 Datasets library. You can easily customize them to your needs if you need extra processing on your datasets.
 
 **Note:** The old script `run_language_modeling.py` is still available [here](https://github.com/huggingface/transformers/blob/master/examples/legacy/run_language_modeling.py).
 
@@ -60,6 +59,15 @@ python run_clm.py \
     --output_dir /tmp/test-clm
 ```
 
+This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_clm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:
+
+```bash
+python run_clm_no_trainer.py \
+    --dataset_name wikitext \
+    --dataset_config_name wikitext-2-raw-v1 \
+    --model_name_or_path gpt2 \
+    --output_dir /tmp/test-clm
+```
 
 ### RoBERTa/BERT/DistilBERT and masked language modeling
 
@@ -95,23 +103,33 @@ python run_mlm.py \
 If your dataset is organized with one sample per line, you can use the `--line_by_line` flag (otherwise the script
 concatenates all texts and then splits them in blocks of the same length).
 
+This uses the built in HuggingFace `Trainer` for training. If you want to use a custom training loop, you can utilize or adapt the `run_mlm_no_trainer.py` script. Take a look at the script for a list of supported arguments. An example is shown below:
+
+```bash
+python run_mlm_no_trainer.py \
+    --dataset_name wikitext \
+    --dataset_config_name wikitext-2-raw-v1 \
+    --model_name_or_path roberta-base \
+    --output_dir /tmp/test-mlm
+```
+
 **Note:** On TPU, you should use the flag `--pad_to_max_length` in conjunction with the `--line_by_line` flag to make
 sure all your batches have the same length.
 
 ### Whole word masking
 
-This part was moved to `examples/research_projects/mlm_wwm`. 
+This part was moved to `examples/research_projects/mlm_wwm`.
 
 ### XLNet and permutation language modeling
 
-XLNet uses a different training objective, which is permutation language modeling. It is an autoregressive method 
-to learn bidirectional contexts by maximizing the expected likelihood over all permutations of the input 
+XLNet uses a different training objective, which is permutation language modeling. It is an autoregressive method
+to learn bidirectional contexts by maximizing the expected likelihood over all permutations of the input
 sequence factorization order.
 
-We use the `--plm_probability` flag to define the ratio of length of a span of masked tokens to surrounding 
+We use the `--plm_probability` flag to define the ratio of length of a span of masked tokens to surrounding
 context length for permutation language modeling.
 
-The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used 
+The `--max_span_length` flag may also be used to limit the length of a span of masked tokens used
 for permutation language modeling.
 
 Here is how to fine-tune XLNet on wikitext-2:

examples/language-modeling/run_mlm_no_trainer.py

@@ -56,7 +56,7 @@
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
+    parser = argparse.ArgumentParser(description="Finetune a transformers model on a Masked Language Modeling task")
     parser.add_argument(
         "--dataset_name",
         type=str,