Update README.md

README.md

@@ -9,4 +9,216 @@ tags:
 - sentence_simplification
 - simplification
 - text2text
----
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+# simplify_dutch
+
+This is the source code for my thesis on "Controllable Sentence Simplification in Dutch" 
+in the Masters of AI at KU Leuven. The full code can be found at: https://github.com/tsei902/simplify_dutch
+
+# Data
+The origin of the datasets in resources/datasets is: 
+1) Wikilarge, available under: https://github.com/XingxingZhang/dress
+The wikilarge data is limited the first 10000 rows. 
+
+2) ASSET, available under: https://github.com/facebookresearch
+Which both have been translated to Dutch. 
+
+# Model
+The Dutch T5 model t5-base-dutch from Hugging Face has been adopted and trained on the task 
+of sentence simplification. 
+The folder /saved model contains the final trained model on 10000 rows of data, as stated in the Thesis. 
+
+# Sequence: 
+1) TRAINING DATA needs preprocessing with preprocessor.py
+2) Generation can be done with generate_on_pretrained.py with a prior adjustment of 
+3) The generation parameters in model.simplify() where the decoding method needs to be chosen (Greedy decoding, Top-p & top-k, or Beam search)
+4) Manual scoring of a generated text is possible with evaluate.py
+
+# Further remarks: 
+1) The folder resources/processed data contains the training set with the prepended control tokens
+2) The folder resources/DUMPS contains the Word embeddings from Fares et al. (2017) have been used. The data is available under: http://vectors.nlpl.eu/repository. (Fares, M., Kutuzov, A., Oepen, S., & Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. Proceedings of the 21st Nordic Conference on Computational Linguistics, Gothenburg, Sweden.)
+3) The folder resources/outputs/final_decoder_outputs contains the final generated text per decoding strategy (Greedy decoding, Top-p & top-k, or Beam search) for both the full test set and the sample dataset
+4) The folder translations contains sampled text (106 and 84 rows) from the original English datasets (WIKILarge and ASSET), a machine-translated version as well as the human translated references. 
+
+# Credits
+The preprocessor.py and the utils.py contain code that has been adapted from https://github.com/KimChengSHEANG/TS_T5 (Sheang, K. C., & Saggion, H. (2021). Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer.INLG 2021 International Conference on Natural Language Generation, Aberdeen, Scotland, UK.)
+The preprocessor.py has been adapted to the usage of Dutch.
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** Theresa Seidl
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** Dutsch
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** https://huggingface.co/yhavinga/t5-base-dutch
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/tsei902/simplify_dutch
+- **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. -->
+
+[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 section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[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]
+
+## 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
+
+<!-- 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]
+
+## 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]
+