Proofread README.md (#28)

Basic proofreading for typos and punctuation errors.

README.md

@@ -2,11 +2,11 @@
 
 A robust Python tool for text-based AI training and generation using [OpenAI's](https://openai.com) [GPT-2](https://openai.com/blog/better-language-models/) architecture.
 
-aitextgen is a Python package that leverages [PyTorch](https://pytorch.org), [Huggingface Transformers](https://github.com/huggingface/transformers) and [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning) with specific optimizations for text generation using GPT-2, plus _many_ added features. It is the successor to [textgenrnn](https://github.com/minimaxir/textgenrnn) and [gpt-2-simple](https://github.com/minimaxir/gpt-2-simple), taking the best of both packages:
+aitextgen is a Python package that leverages [PyTorch](https://pytorch.org), [Hugging Face Transformers](https://github.com/huggingface/transformers) and [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning) with specific optimizations for text generation using GPT-2, plus _many_ added features. It is the successor to [textgenrnn](https://github.com/minimaxir/textgenrnn) and [gpt-2-simple](https://github.com/minimaxir/gpt-2-simple), taking the best of both packages:
 
 - Finetunes on a pretrained 124M GPT-2 model from OpenAI...or create your own GPT-2 model + tokenizer and train from scratch!
 - Generates text faster than gpt-2-simple and with better memory efficiency! (even [from the 1.5B GPT-2 model](https://docs.aitextgen.io/tutorials/generate_1_5b/)!)
-- With Transformers, aitextgen preserves compatibility with the base package, allowing you to use the model for other NLP tasks, download custom GPT-2 models from the Huggingface model repository, and upload your own models! Also, it uses the included `generate()` function to allow a massive amount of control over the generated text.
+- With Transformers, aitextgen preserves compatibility with the base package, allowing you to use the model for other NLP tasks, download custom GPT-2 models from the Hugging Face model repository, and upload your own models! Also, it uses the included `generate()` function to allow a massive amount of control over the generated text.
 - With pytorch-lightning, aitextgen trains models not just on CPUs and GPUs, but also _multiple_ GPUs and (eventually) TPUs! It also includes a pretty training progress bar, with the ability to add optional loggers.
 - The input dataset is its own object, allowing you to not only easily encode megabytes of data in seconds, cache, and compress it on a local computer before transporting to a remote server, but you are able to _merge_ datasets without biasing the resulting dataset, or _cross-train_ on multiple datasets to create blended output.
 
@@ -54,7 +54,7 @@ aitextgen generate
 aitextgen generate --prompt "I believe in unicorns because" --to_file False
 ```
 
-Want to train your own mini GPT-2 model on your own computer? Download this [text file of Shakespeare plays](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt), cd to that directory in a Teriminal, open up a `python3` console and go:
+Want to train your own mini GPT-2 model on your own computer? Download this [text file of Shakespeare's plays](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt), cd to that directory in a Terminal, open up a `python3` console and go:
 
 ```python
 from aitextgen.TokenDataset import TokenDataset
@@ -95,7 +95,7 @@ Want to run aitextgen and finetune GPT-2? Use the Colab notebooks in the Demos s
 
 ## Known Issues
 
-- TPUs cannot be used to train a model: although you _can_ train an aitextgen model on TPUs by setting `n_tpu_cores=8` in an appropriate runtime, and the training loss indeed does decrease, there are a number of miscellaneous blocking problems. [[Tracking GitHub Issue](https://github.com/minimaxir/aitextgen/issues/3)].
+- TPUs cannot be used to train a model: although you _can_ train an aitextgen model on TPUs by setting `n_tpu_cores=8` in an appropriate runtime, and the training loss indeed does decrease, there are a number of miscellaneous blocking problems. [[Tracking GitHub Issue](https://github.com/minimaxir/aitextgen/issues/3)]
 - TorchScript exporting, although it works with `ai.export()`, behaves oddly when reloaded back into Python, and is therefore not supported (yet). [[Tracking GitHub Issue](https://github.com/minimaxir/aitextgen/issues/5)]
 - Finetuning the 355M GPT-2 model or larger on a GPU will cause the GPU to go OOM, even 16 GB VRAM GPUs (355M _does_ work with FP16 + 16 GB VRAM however). This is a [known issue with the Transformers GPT-2 implementation](https://github.com/huggingface/transformers/pull/2356), unfortunately. Gradient checkpointing may need to be implemented within the training loop of aitextgen. [[Tracking GitHub Issue](https://github.com/minimaxir/aitextgen/issues/6)]
   - As a temporary workaround, you can finetune larger models with [gpt-2-simple](https://github.com/minimaxir/gpt-2-simple), then [convert the TensorFlow weights to a PyTorch model](https://docs.aitextgen.io/gpt-2-simple/).
@@ -104,19 +104,19 @@ Want to run aitextgen and finetune GPT-2? Use the Colab notebooks in the Demos s
 
 The current release (v0.2.X) of aitextgen **is considered to be a beta**, targeting the most common use cases. The Notebooks and examples written so far are tested to work, but more fleshing out of the docs/use cases will be done over the next few months in addition to fixing the known issues noted above.
 
-The next versions of aitextgen (and one of the reasons I made this package in the first place) will have native support for _schema-based generation_. (see [this repo](https://github.com/minimaxir/gpt-2-keyword-generation) for a rough proof-of-concept)
+The next versions of aitextgen (and one of the reasons I made this package in the first place) will have native support for _schema-based generation_. (See [this repo](https://github.com/minimaxir/gpt-2-keyword-generation) for a rough proof-of-concept.)
 
-Additionally, I plan to develop an aitextgen [SaaS](https://en.wikipedia.org/wiki/Software_as_a_service) to allow anyone to run aitextgen in the cloud and build APIs/Twitter+Slack+Discord bots with just a few clicks. (the primary constraint is compute cost; if any venture capitalists are interested in funding the development of such a service, let me know)
+Additionally, I plan to develop an aitextgen [SaaS](https://en.wikipedia.org/wiki/Software_as_a_service) to allow anyone to run aitextgen in the cloud and build APIs/Twitter+Slack+Discord bots with just a few clicks. (The primary constraint is compute cost; if any venture capitalists are interested in funding the development of such a service, let me know.)
 
-I've listed more tenative features in the [UPCOMING](UPCOMING.md) document.
+I've listed more tentative features in the [UPCOMING](UPCOMING.md) document.
 
 ## Ethics
 
 aitextgen is a tool primarily intended to help facilitate creative content. It is not a tool intended to deceive. Although parody accounts are an obvious use case for this package, make sure you are _as upfront as possible_ with the methodology of the text you create. This includes:
 
-- State that the text was generated using aitextgen and/or a GPT-2 model architecture. (a link to this repo would be a bonus!)
+- State that the text was generated using aitextgen and/or a GPT-2 model architecture. (A link to this repo would be a bonus!)
 - If parodying a person, explicitly state that it is a parody, and reference who it is parodying.
-- If the generated text is human-curated, or if it's unsupervised random output
+- If the generated text is human-curated, or if it's unsupervised random output.
 - Indicating who is maintaining/curating the AI-generated text.
 - Make a good-faith effort to remove overfit output from the generated text that matches the input text verbatim.