upload read me with all training params (manual)

README.md

@@ -48,30 +48,26 @@ model-index:
 
 # Model Card for Musterdatenkatalog Classifier
 
-<!-- Provide a quick summary of what the model is/does. -->
-
 # Model Details
 
 ## Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
 This model is based on bert-base-german-cased and fine-tuned on and-effect/mdk_gov_data_titles_clf. This model reaches an accuracy of XY on the test set and XY on the validation set
 
 - **Developed by:** and-effect
 - **Shared by:** [More Information Needed]
 - **Model type:** Text Classification
 - **Language(s) (NLP):** de
-- **License:** XY
-- **Finetuned from model:** bert-base-german-case. For more information one the model check on [this model card](https://huggingface.co/bert-base-german-cased)
+- **License:** [More Information Needed]
+- **Finetuned from model:** "bert-base-german-case. For more information one the model check on [this model card](https://huggingface.co/bert-base-german-cased)"
 
 ## Model Sources 
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** XY git hub repo?
-- **Paper:** website bst?
-- **Demo:** XY Spaces? 
+- **Repository:** [More Information Needed]
+- **Paper:** [More Information Needed]
+- **Demo:** [More Information Needed]
 
 # Direct Use
 
@@ -136,16 +132,15 @@ print(sentence_embeddings)
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 The model is intended to classify open source dataset titles from german municipalities. More information on the Taxonomy (classification categories) and the Project can be found on XY.
-For more information see Github Repo + Spaces
+
+[More Information Needed on downstream_use_demo]
 
 # Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-The model has some limititations. The model has some limitations in terms of the downstream task. 
- 1. **Distribution of classes**: The dataset trained on is small, but at the same time the number of classes is very high. Thus, for some classes there are only a few examples (more information about the class distribution of the training data can be found here). Consequently, the performance for smaller classes may not be as good as for the majority classes. Accordingly, the evaluation is also limited. 
- 2. **Systematic problems**: some subjects could not be correctly classified systematically. One example is the embedding of titles containing 'Corona'. In none of the evaluation cases could the titles be embedded in such a way that they corresponded to their true names.  Another systematic example is the embedding and classification of titles related to 'migration'. 
- 3. **Generalization of the model**: by using semantic search, the model is able to classify titles into new categories that have not been trained, but the model is not tuned for this and therefore the performance of the model for unseen classes is likely to be limited.
+The model has some limititations. The model has some limitations in terms of the downstream task. \n 1. **Distribution of classes**: The dataset trained on is small, but at the same time the number of classes is very high. Thus, for some classes there are only a few examples (more information about the class distribution of the training data can be found here). Consequently, the performance for smaller classes may not be as good as for the majority classes. Accordingly, the evaluation is also limited. \n 2. **Systematic problems**: some subjects could not be correctly classified systematically. One example is the embedding of titles containing 'Corona'. In none of the evaluation cases could the titles be embedded in such a way that they corresponded to their true names.  Another systematic example is the embedding and classification of titles related to 'migration'. \n 3. **Generalization of the model**: by using semantic search, the model is able to classify titles into new categories that have not been trained, but the model is not tuned for this and therefore the performance of the model for unseen classes is likely to be limited.
+
 
 ## Recommendations
 
@@ -161,13 +156,21 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 You can find all information about the training data [here](https://huggingface.co/datasets/and-effect/mdk_gov_data_titles_clf). For the Fine Tuning we used the revision 172e61bb1dd20e43903f4c51e5cbec61ec9ae6e6 of the data, since the performance was better with this previous version of the data.
 
-## Training Procedure [optional]
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Training Procedure
 
 ### Preprocessing
 
-[More Information Needed]
+This section describes the generating of the input data for the model. More information on the preprocessing of the data itself can be found [here](https://huggingface.co/datasets/and-effect/mdk_gov_data_titles_clf)
+
+The model is fine tuned with similar and dissimilar pairs. Similar pairs are built with all titles and their true label. Dissimilar pairs defined as pairs of title and all labels, except the true label. Since the combinations of dissimilar is much higher, a sample of two pairs per title is selected.
+
+| pairs | size |
+|-----|-----|
+| train_similar_pairs | 2018 |
+| train_unsimilar_pairs | 1009 |
+| test_similar_pairs | 498 |
+| test_unsimilar_pairs | 249 |
+
 
 ## Training Parameter
 The model was trained with the parameters:
@@ -181,16 +184,8 @@ The model was trained with the parameters:
 Hyperparameter:
 ```
 {
-    "epochs": [More Information Needed],
-    "evaluation_steps": 0,
-    "evaluator": NoneType,
-    "max_grad_norm": 1,
-    "optimizer_class": <class 'torch.optim.adamw.AdamW'>,
-    "optimizer_params": {'learning rate': 2e-05},
-    "scheduler": WarmupLinear,
-    "steps_per_epoch": null,
-    "warmup_steps": 100,
-    "weight_decay":0.01
+    "epochs": 3,
+    "warumup_steps": [More Information Needed],
 }
 ```
 
@@ -203,21 +198,16 @@ Hyperparameter:
 
 # Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+All metrices express the models ability to classify dataset titles from GOVDATA into the taxonomy described [here](https://huggingface.co/datasets/and-effect/mdk_gov_data_titles_clf). For more information see VERLINKUNG MDK Projekt. 
 
 ## Testing Data, Factors & Metrics
 
 ### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
 The evaluation data can be found [here](https://huggingface.co/datasets/and-effect/mdk_gov_data_titles_clf). Since the model is trained on revision 172e61bb1dd20e43903f4c51e5cbec61ec9ae6e6 for evaluation, the evaluation metrics rely on the same revision.
 
 
 ### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
 The model performance is tested with fours metrices. Accuracy, Precision, Recall and F1 Score. A lot of classes were not predicted and are thus set to zero for the calculation of precision, recall and f1 score. For these metrices the additional calucations were performed exluding classes with less than two predictions for the level 'Bezeichnung' (see in table results 'Bezeichnung II'. Although intepretation of these results should be interpreted with caution, because they do not represent all classes.
 
 ## Results
@@ -229,7 +219,7 @@ The model performance is tested with fours metrices. Accuracy, Precision, Recall
 | Test dataset 'Bezeichnung' II | 0.7004405286343612 | 0.573015873015873 | 0.8207602339181287 | 0.6515010351966875 |
 | Validation dataset 'Bezeichnung' I | 0.5445544554455446 | 0.41787439613526567 | 0.39929183135704877 | 0.4010173484686228 |
 | Validation dataset 'Thema' I | 0.801980198019802 | 0.6433080808080808 | 0.7039711632453568 | 0.6591710279769981 |
-| Validation dataset 'Bezeichnung' II | 0.5445544554455446 | 0.6018518518518517 | 0.6278409090909091 | 0.6066776135741653 |
+| Validation dataset 'Bezeichnung' II | 0.5445544554455446 | 0.6018518518518519 | 0.6278409090909091 | 0.6066776135741653 |
 
 
 ### Summary