Update README.md

README.md

@@ -7,60 +7,95 @@ metrics:
 - accuracy
 pipeline_tag: text-classification
 ---
-Model Description
-Given 2 words in Turkish, the model predicts whether they share an affix or not. Fine-tuned on dbmdz/bert-base-turkish-cased, fine-tuned on a task similar to NLI, but on word level and with 2 labels. It was created as a final project for one of my classes.
-
-Developed by: Scoup123
-Model type: BERT
-Language(s) (NLP): Turkish
-Finetuned from model [optional]: dbmdz/bert-base-turkish-cased
-Model Sources [optional]
-Repository: [More Information Needed]
-Paper [optional]: in-works
-Uses
+# Model Card for Model ID
+
+### Model Description
+Given 2 words in Turkish, the model predicts whether they share an affix or not. Fine-tuned on dbmdz/bert-base-turkish-cased,
+fine-tuned on a task similar to NLI, but on word level and with 2 labels. It was created as a final project for one of my classes.
+
+
+
+- **Developed by:** Scoup123
+- **Model type:** BERT
+- **Language(s) (NLP):** Turkish
+- **Finetuned from model [optional]:** dbmdz/bert-base-turkish-cased
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** in-works
+-
+
+## Uses
+
 It can be used in morphological analyzing tasks.
+### Direct Use
 
-Direct Use
 It can probably be used without additional finetuning on Turkish.
 
-Training Details
-Training Data
+## Training Details
+
+### Training Data
+
 scoup123/affixfinder
 
 The dataset used was generated from a generated dataset mentioned in the paper titled Turkish language resources: Morphological parser, morphological disambiguator and web corpus.
 
-Evaluation
-Test Accuracy: 0.9874 Precision: 0.9874 Recall: 0.9874 F1 Score: 0.9874
+
+## Evaluation
+
+Test Accuracy: 0.9874
+Precision: 0.9874
+Recall: 0.9874
+F1 Score: 0.9874
 
 **It should be used with caution as these scores are too high.
 
-Testing Data, Factors & Metrics
-Testing Data
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
 A testing split data was created from the training data
 
-Summary
-This model aims to create an affix identifier for Turkish.
+#### Summary
+
+This model aims to create an affix identifier for Turkish. 
+
+## Model Examination [optional]
 
-Model Examination [optional]
 I have just created it, so further testing needed to check if it actually works. Additionally, you should check it if it works before using it.
 
 [More Information Needed]
 
-Environmental Impact
-Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
+## Environmental Impact
+
+
+
+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:** Free Colab T4 GPU
+- **Hours used:** ~2.5 hours
+- **Cloud Provider:** Google
+- **Compute Region:** Europe
+- **Carbon Emitted:** [More Information Needed]
 
-Hardware Type: Free Colab T4 GPU
-Hours used: ~2.5 hours
-Cloud Provider: Google
-Compute Region: Europe
-Carbon Emitted: [More Information Needed]
-Citation [optional]
-APA:
 
-Sak, H., Güngör, T., & Saraçlar, M. (2008). Turkish language resources: Morphological parser, morphological disambiguator and web corpus. In Advances in natural language processing (pp. 417-427). Springer Berlin Heidelberg.
+## Citation [optional]
+
+**APA:**
+
+Sak, H., Güngör, T., & Saraçlar, M. (2008). Turkish language resources: Morphological parser, morphological disambiguator and web corpus. 
+In Advances in natural language processing (pp. 417-427). Springer Berlin Heidelberg.
+
+
+
+
+## Model Card Authors [optional]
 
-Model Card Authors [optional]
 Kaan Bayar
 
-Model Card Contact
+## Model Card Contact
+
 kaan.bayar13@gmail.com