Update README.md

README.md

@@ -1,30 +1,93 @@
 ---
 license: cc-by-nc-sa-4.0
 tags:
-- generated_from_trainer
-model-index:
-- name: grammar-synthesis-base-V1-ft3-jflAUG-v5
-  results: []
+- grammar
+- spelling
+- punctuation
+- error-correction
+datasets:
+- jfleg
+widget:
+- text: "i can has cheezburger"
+  example_title: "cheezburger"
+- text: "There car broke down so their hitching a ride to they're class."
+  example_title: "compound-1"
+- text: "so em if we have an now so with fito ringina know how to estimate the tren given the ereafte mylite trend we can also em an estimate is nod s
+i again tort watfettering an we have estimated the trend an
+called wot to be called sthat of exty right now we can and look at
+wy this should not hare a trend i becan we just remove the trend an and we can we now estimate
+tesees ona effect of them exty"
+  example_title: "Transcribed Audio Example 2"
+- text: "My coworker said he used a financial planner to help choose his stocks so he wouldn't loose money."
+  example_title: "incorrect word choice (context)"
+- text: "good so hve on an tadley i'm not able to make it to the exla session on monday this week e which is why i am e recording pre recording
+an this excelleision and so to day i want e to talk about two things and first of all em i wont em wene give a summary er about
+ta ohow to remove trents in these nalitives from time series"
+  example_title: "lowercased audio transcription output"
+
+parameters:
+  max_length: 128
+  min_length: 2
+  num_beams: 8
+  repetition_penalty: 1.3
+  length_penalty: 1
+  early_stopping: True
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
-# grammar-synthesis-base-V1-ft3-jflAUG-v5
+# grammar-synthesis-base (beta)
 
-This model is a fine-tuned version of [pszemraj/grammar-synthesis-base-V1](https://huggingface.co/pszemraj/grammar-synthesis-base-V1) on an unknown dataset.
+This model is a fine-tuned version of [google/t5-base-lm-adapt](https://huggingface.co/google/t5-base-lm-adapt) for grammar correction on an expanded version of the [JFLEG](https://paperswithcode.com/dataset/jfleg) dataset.
+
+usage in Python (after `pip install transformers`):
+
+```
+from transformers import pipeline
+corrector = pipeline(
+              'text2text-generation',
+              'pszemraj/grammar-synthesis-base',
+              )
+raw_text = 'i can has cheezburger'
+results = corrector(raw_text)
+print(results)
+```
 
 ## Model description
 
-More information needed
+The intent is to create a text2text language model that successfully completes "single-shot grammar correction" on a potentially grammatically incorrect text **that could have a lot of mistakes** with the important qualifier of **it does not semantically change text/information that IS grammatically correct.**
+
+Compare some of the heavier-error examples on [other grammar correction models](https://huggingface.co/models?dataset=dataset:jfleg) to see the difference :)
+
+## Limitations
+
+- dataset: `cc-by-nc-sa-4.0`
+- model: `apache-2.0`
+- this is **still a work-in-progress** and while probably useful for "single-shot grammar correction" in a lot of cases, **give the outputs a glance for correctness ok?**
+
+## Use Cases
+
+Obviously, this section is quite general as there are many things one can use "general single-shot grammar correction" for. Some ideas or use cases:
 
-## Intended uses & limitations
+1. Correcting highly error-prone LM outputs. Some examples would be audio transcription (ASR) (this is literally some of the examples) or something like handwriting OCR. 
+    - To be investigated further, depending on what model/system is used it _might_ be worth it to apply this after OCR on typed characters. 
+2. Correcting/infilling text generated by text generation models to be cohesive/remove obvious errors that break the conversation immersion. I use this on the outputs of [this OPT 2.7B chatbot-esque model of myself](https://huggingface.co/pszemraj/opt-peter-2.7B).
+  > An example of this model running on CPU with beam search:
+  
+```
+original response:
+                ive heard it attributed to a bunch of different philosophical schools, including stoicism, pragmatism, existentialism and even some forms of post-structuralism. i think one of the most interesting (and most difficult) philosophical problems is trying to let dogs (or other animals) out of cages. the reason why this is a difficult problem is because it seems to go against our grain (so to
+synthesizing took 306.12 seconds
+Final response in 1294.857 s:
+        I've heard it attributed to a bunch of different philosophical schools, including solipsism, pragmatism, existentialism and even some forms of post-structuralism. i think one of the most interesting (and most difficult) philosophical problems is trying to let dogs (or other animals) out of cages. the reason why this is a difficult problem is because it seems to go against our grain (so to speak)
+```
+  _Note: that I have some other logic that removes any periods at the end of the final sentence in this chatbot setting [to avoid coming off as passive aggressive](https://www.npr.org/2020/09/05/909969004/before-texting-your-kid-make-sure-to-double-check-your-punctuation)_
+  
+3. Somewhat related to #2 above, fixing/correcting so-called [tortured-phrases](https://arxiv.org/abs/2107.06751) that are dead giveaways text was generated by a language model. _Note that _SOME_ of these are not fixed, especially as they venture into domain-specific terminology (i.e. irregular timberland instead of Random Forest)._
 
-More information needed
 
 ## Training and evaluation data
 
-More information needed
+More information needed 😉
 
 ## Training procedure
 
@@ -41,7 +104,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.02
-- num_epochs: 1
+- num_epochs: 2
 
 ### Training results