update readme

README.md

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 ---
-library_name: peft
 base_model: mistralai/Mistral-7B-v0.3
 license: agpl-3.0
 ---
 
-# Model Card for Model ID
+# Open Food Facts - Ingredients spellcheck model
 
-<!-- Provide a quick summary of what the model is/does. -->
+When a product is added to the database, all its details, such as allergens, additives, or nutritional values, are either wrote down by the contributor, or automatically extracted from the product pictures using OCR.
 
+However, it often happens the information extracted by OCR contains typos and errors due to bad quality pictures: low-definition, curved product, light reflection, etc...
+
+To solve this problem, we developed an **Ingredient Spellcheck** 🍊, a model capable of correcting typos in a list of ingredients following a defined guideline. The model, based on [Mistral-7B-v0.3], was fine-tuned on thousand of corrected lists of ingredients extracted from the database. 
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+The Open Food Facts Ingredients Spellcheck is a version of [Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) fine-tuned on thousands of corrected list of ingredients extracted from the OFF database.
 
+The training dataset, with the evaluation benchmark are available in the Open Food Facts HF repository:
 
-- **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]
+* **Training dataset:** https://huggingface.co/datasets/openfoodfacts/spellcheck-dataset
+* **Evaluation benchmark:** https://huggingface.co/datasets/openfoodfacts/spellcheck-benchmark 
 
-### Model Sources [optional]
+The project is currently in development. You can find it in the Open Food Facts Github repo. 
 
-<!-- Provide the basic links for the model. -->
+A demo of this model is also available in HF Spaces. 
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/openfoodfacts/openfoodfacts-ai/tree/develop/spellcheck
+- **Demo:** https://huggingface.co/spaces/jeremyarancio/ingredients-spellcheck
 
 ## 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. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
+This model takes a list of ingredients of a product as input and returns the correction. 
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+It follows a spellcheck guideline, which was used to build the training and evaluation datasets. You can find this guideline in the [Spellcheck project README](https://github.com/openfoodfacts/openfoodfacts-ai/tree/spellcheck/spellcheck).
 
-[More Information Needed]
+To respect the training process, the input list of ingredients needs to be embedded into the following prompt:
 
-## Bias, Risks, and Limitations
+```python
+def prepare_instruction(text: str) -> str:
+    """Prepare instruction prompt for fine-tuning and inference.
+    Identical to instruction during training.
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+    Args:
+        text (str): List of ingredients
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+    Returns:
+        str: Instruction.
+    """
+    instruction = (
+        "###Correct the list of ingredients:\n"
+        + text
+        + "\n\n###Correction:\n"
+    )
+    return instruction
+```
 
 ## Training Details
 
-### Training Data
-
-<!-- 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 
+The model training informations are available in the [CometML Experiment Tracker](https://www.comet.com/jeremyarancio/spellcheck/e223b404168f4d4c8e633cbd0909b60d?compareXAxis=step&experiment-tab=panels&showOutliers=true&smoothing=0&viewId=vhfLDppdrZXnthxtP5Lnb3tep&xAxis=step), along the other experimentations.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+The model was trained on AWS Sagemaker using an ml.g5.2xlarge instance for 3 epochs.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[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]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+The model is evaluated on the [benchmark](https://huggingface.co/datasets/openfoodfacts/spellcheck-benchmark) using a custom evaluation algorithm.
 
-## Model Card Contact
+In short, lists of ingredients are separated into 3 parts: *original*, *reference*, *prediction*. Using a [sequence alignement algorithm](https://en.wikipedia.org/wiki/Sequence_alignment) between respectively *original*-*reference* and *original*-**prediction*, we are able to tell which token were supposed to be corrected, and which one was actually corrected. This leads to a correction Precision and Recall.
 
-[More Information Needed]
+The complete explanation of the algorithm is available in the [Spellchech README](https://github.com/openfoodfacts/openfoodfacts-ai/tree/spellcheck/spellcheck).
 
+### Metrics:
+* Correction precision: **0.67**
+* Correction recall: **0.62**
+* Localisation precision: **0.75**
+* Localisation recall: **0.69**
 
-### Framework versions
+## Additional links:
 
-- PEFT 0.8.2
+* Open Food Facts website: https://world.openfoodfacts.org/discover
+* Open Food Facts Github: https://github.com/openfoodfacts