Merge pull request #6 from thinkst/sara/minor-improvements

Minor improvements in docs

README.md

@@ -2,7 +2,7 @@
 
 This is a research repo for fast AI detection using compression.
 While there are a number of existing LLM detection systems, they all use a large model trained on either an LLM or
-its training data to calculate the probability of each word given the preceeding, then calculating a score where
+its training data to calculate the probability of each word given the preceding, then calculate a score where
 the more high-probability tokens are more likely to be AI-originated. Techniques and tools in this repo are looking for
 faster approximation to be embeddable and more scalable.
 
@@ -11,10 +11,10 @@ faster approximation to be embeddable and more scalable.
 ZipPy uses either the LZMA or zlib compression ratios as a way to indirectly measure the perplexity of a text.
 Compression ratios have been used in the past to [detect anomalies in network data](http://owncloud.unsri.ac.id/journal/security/ontheuse_compression_Network_anomaly_detec.pdf)
 for intrusion detection, so if perplexity is roughly a measure of anomalous tokens, it may be possible to use compression to detect low-perplexity text.
-LZMA and zlib creates a dictionary of seen tokens, and then uses though in place of future tokens. The dictionary size, token length, etc.
+LZMA and zlib create a dictionary of seen tokens and then use though in place of future tokens. The dictionary size, token length, etc.
 are all dynamic (though influenced by the 'preset' of 0-9--with 0 being the fastest but worse compression than 9). The basic idea
 is to 'seed' a compression stream with a corpus of AI-generated text (`ai-generated.txt`) and then measure the compression ratio of 
-just the seed data with that of the sample appended. Samples that follow more closely in word choice, structure, etc. will acheive a higher 
+just the seed data with that of the sample appended. Samples that follow more closely in word choice, structure, etc. will achieve a higher 
 compression ratio due to the prevalence of similar tokens in the dictionary, novel words, structures, etc. will appear anomalous to the seeded
 dictionary, resulting in a worse compression ratio.
 
@@ -28,7 +28,7 @@ Here are each of them compared with both the LZMA and zlib detector across the t
 
 ### Usage
 
-ZipPy will read files passed as command-line arguments, or will read from stdin to allow for piping of text to it. 
+ZipPy will read files passed as command-line arguments or will read from stdin to allow for piping of text to it. 
 
 First, build and install the tool:
 ```