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README.md

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-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
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-
 # Nougat for formula
 
 <!-- Provide a quick summary of what the model is/does. -->
 
 We performed fune-tuning on [small-sized Nougat model](https://huggingface.co/facebook/nougat-small) using data
-from [IM2LATEX-100K](https://www.kaggle.com/datasets/shahrukhkhan/im2latex100k) and luckily got good results.
+from [IM2LATEX-100K](https://www.kaggle.com/datasets/shahrukhkhan/im2latex100k) to make it especially powerful in
+identifying formula from images.
 
 ## Model Details
 
@@ -17,63 +12,64 @@ from [IM2LATEX-100K](https://www.kaggle.com/datasets/shahrukhkhan/im2latex100k)
 
 <!-- Provide a longer summary of what this model is. -->
 
+Nougat for formula is good at identifying formula from images. It takes images with white backgroud and formula written in
+black as input and return with accurate Latex code for the formula.
 
+The Naugat model (Neural Optical Understanding for Academic Documents) was proposed by Meta AI in August 2023 as
+a visual Transformer model for processing scientific documents. It can convert PDF format documents into Markup language,
+especially with good recognition ability for mathematical expressions and tables.The goal of this model is to improve the accessibility
+of scientific knowledge by bridging human readable documents with machine readable text.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+- **Model type:** Vision Encoder Decoder
+- **Finetuned from model:** [Nougat model, small-sized version](https://huggingface.co/facebook/nougat-small)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Nougat for formula can be used as a tool for converting complicated formula to Latex code. It has potential to be
+a good substitute for other tools. 
 
-[More Information Needed]
+For example, when you are taking notes and tired at coding long Latex/Markdown formula code, just make a screen shot
+of them and put it into Nougat for formula. Then you can get the exact code for the formula as long as it won't exceed
+the max length of the model you use.
 
-### Downstream Use [optional]
+You can also continue fine-tuning the model to make it more powerful in identifying formulas from certain subjects.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Nougat for formula may be useful when developing tools or apps aiming at generating Latex code.
 
-[More Information Needed]
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
 
-## Bias, Risks, and Limitations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Demo below shows how to input an image into the model and generate Latex/Markdown formula code.
 
-[More Information Needed]
+```
+from transformers import NougatProcessor, VisionEncoderDecoderModel
+from PIL import Image
 
-### Recommendations
+max_length = 100 # defing max length of output
+processor = NougatProcessor.from_pretrained(r".", max_length = max_length) # Replace with your path 
+model = VisionEncoderDecoderModel.from_pretrained(r".") # Replace with your path
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+image = Image.open(r"image_path") # Replace with your path
+image = processor(image, return_tensors="pt").pixel_values # The processor will resize the image according to our model
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+result_tensor = model.generate(
+            image,
+            max_length=max_length,
+            bad_words_ids=[[processor.tokenizer.unk_token_id]]
+              ) # generate id tensor
 
-## How to Get Started with the Model
+result = processor.batch_decode(result_tensor, skip_special_tokens=True) # Using the processor to decode the result
+result = processor.post_process_generation(result, fix_markdown=False)
 
-Use the code below to get started with the model.
+print(*result)
+```
 
-[More Information Needed]
 
 ## Training Details
 
@@ -81,26 +77,23 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
-
-### Training Procedure 
+[IM2LATEX-100K](https://www.kaggle.com/datasets/shahrukhkhan/im2latex100k)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
+#### Preprocessing
 
-[More Information Needed]
+The preprocessing of X(image) has been showed in the short demo above.
 
+The preprocessing of Y(formula) is done by:
 
-#### Training Hyperparameters
+1. Remove the space in the formula string.
+2. Using `processor` to tokenize the string.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Speeds, Sizes, Times [optional]
+#### Training Hyperparameters
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- **Training regime:** `torch.optim.AdamW(model.parameters(), lr=1e-4)` <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-[More Information Needed]
 
 ## Evaluation
 
@@ -111,34 +104,22 @@ Use the code below to get started with the model.
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
+The tesing data is also taken from [IM2LATEX-100K](https://www.kaggle.com/datasets/shahrukhkhan/im2latex100k).
+Note that the train, validation and test data has been well split before downloading.
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+BLEU and CER.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
+The BLEU is 0.8157 and CER is 0.1601 on test data.
 
 
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## Environmental Impact