Changed very long line to short multi lines to make it easier for editing in IDE.

README.md

@@ -6,9 +6,12 @@ widget:
 
 # GPT2-medium-indonesian
 
-This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/).
+This is a pretrained model on Indonesian language using a causal language modeling (CLM) objective, which was first 
+introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
+and first released at [this page](https://openai.com/blog/better-language-models/). 
 
-This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) organized by [HuggingFace](https://huggingface.co). All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
+This model was trained using HuggingFace's Flax framework and is part of the [JAX/Flax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104)
+organized by [HuggingFace](https://huggingface.co). All training was done on a TPUv3-8 VM sponsored by the Google Cloud team.
 
 The demo can be found [here](https://huggingface.co/spaces/flax-community/gpt2-indonesian).
 
@@ -50,16 +53,28 @@ output = model(encoded_input)
 ```
 
 ## Limitations and bias 
-The training data used for this model are Indonesian websites of [OSCAR](https://oscar-corpus.com/) and [mc4](https://huggingface.co/datasets/mc4). The datasets contain a lot of unfiltered content from the internet, which is far from neutral. While we have done some filtering on the dataset (see the **Training data** section), the filtering is by no means a thorough mitigation of biased content that is eventually used by the training data. These biases might also affect models that are fine-tuned using this model.
+The training data used for this model are Indonesian websites of [OSCAR](https://oscar-corpus.com/),  
+[mc4](https://huggingface.co/datasets/mc4) and [Wikipedia](https://huggingface.co/datasets/wikipedia). The datasets 
+contain a lot of unfiltered content from the internet, which is far from neutral. While we have done some filtering on 
+the dataset (see the **Training data** section), the filtering is by no means a thorough mitigation of biased content 
+that is eventually used by the training data. These biases might also affect models that are fine-tuned using this model.
 
 As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
 
-> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases that require the generated text to be true.
+> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases 
+> that require the generated text to be true.
 
-> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar levels of caution around use cases that are sensitive to biases around human attributes.
+> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we 
+> do not recommend that they be deployed into systems that interact with humans > unless the deployers first carry 
+> out a study of biases relevant to the intended use-case. We found no statistically significant difference in gender,
+> race, and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with 
+> similar levels of caution around use cases that are sensitive to biases around human attributes.
 
 ## Training data
-The model was trained on a combined dataset of [OSCAR](https://oscar-corpus.com/) and [mc4](https://huggingface.co/datasets/mc4) for the Indonesian language. We have filtered the dataset so that we end up with 29 GB of data in total. The mc4 dataset was cleaned using [this filtering script](https://github.com/Wikidepia/indonesian_datasets/blob/master/dump/mc4/cleanup.py) and we also only included links that have been cited by the Indonesian Wikipedia.
+The model was trained on a combined dataset of [OSCAR](https://oscar-corpus.com/), [mc4](https://huggingface.co/datasets/mc4) 
+and Wikipedia for the Indonesian language. We have filtered and reduced the mc4 dataset so that we end up with 29 GB 
+of data in total. The mc4 dataset was cleaned using [this filtering script](https://github.com/Wikidepia/indonesian_datasets/blob/master/dump/mc4/cleanup.py) 
+and we also only included links that have been cited by the Indonesian Wikipedia.
 
 ## Training procedure 
 The model was trained on a TPUv3-8 VM provided by the Google Cloud team. The training duration was `6d 3h 7m 26s`.
@@ -69,7 +84,7 @@ The model achieves the following results without any fine-tuning (zero-shot):
 
 | dataset | train loss | eval loss | eval perplexity |
 | ---------- | ---------- | -------------- | ---------- |
-| ID OSCAR+mc4 (29GB)      |  2.79     | 2.696         | 14.826   |
+| ID OSCAR+mc4+Wikipedia (29GB)      |  2.79     | 2.696         | 14.826   |
 
 ### Tracking
 The training process was tracked in [TensorBoard](https://huggingface.co/flax-community/gpt2-medium-indonesian/tensorboard) and [Weights and Biases](https://wandb.ai/wandb/hf-flax-gpt2-indonesian?workspace=user-cahya).
@@ -81,4 +96,9 @@ The training process was tracked in [TensorBoard](https://huggingface.co/flax-co
 - Galuh Sahid ([@Galuh](https://huggingface.co/Galuh))
 - Muhammad Agung Hambali ([@AyameRushia](https://huggingface.co/AyameRushia))
 - Muhammad Fhadli ([@muhammadfhadli](https://huggingface.co/muhammadfhadli))
-- Samsul Rahmadani ([@munggok](https://huggingface.co/munggok))
+- Samsul Rahmadani ([@munggok](https://huggingface.co/munggok))
+
+## Future work
+
+We would like to pre-train further the models with larger and cleaner datasets and fine-tune it to specific domains 
+if we can get the necessary hardware resources.