README update with new jinja template

README.md

@@ -1,21 +1,57 @@
 ---
-pipeline_tag: sentence-similarity
+language: de
+library_name: sentence_transformers
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
-
+- text-classification
+model-index:
+- name: and-effect/musterdatenkatalog_clf
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: mdk_gov_data_titles_clf
+      type: and-effect/mdk_gov_data_titles_clf
+    metrics:
+    - type: Accuracy (Bezeichnung)
+      value: 0.7
+    - type: Precision macro (Bezeichnung)
+      value: 0.5
 ---
 
-# {MODEL_NAME}
+# Model Card for Model ID
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+<!-- 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 and accuracy of XY on the test set and XY on the validation set
+
+- **Developed by:** and-effect
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** Text Classification
+- **Language(s) (NLP):** de
+- **License:** XY
+- **Finetuned from model [optional]:** bert-base-german-case. For more information one the model check on [this model card](https://huggingface.co/bert-base-german-cased)
+
+## Model Sources [optional]
 
-<!--- Describe your model here -->
+<!-- Provide the basic links for the model. -->
 
-## Usage (Sentence-Transformers)
+- **Repository:** XY git hub repo?
+- **Paper [optional]:** XY and-effect papers?
+- **Demo [optional]:** XY Spaces? 
 
+# Direct Use
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+## Get Started with Sentence Transformers
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
@@ -33,9 +69,7 @@ embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-
-
-## Usage (HuggingFace Transformers)
+## Get Started with HuggingFace Transformers
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
 ```python
@@ -71,56 +105,144 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
+# Downstream Use
 
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-## Evaluation Results
+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
 
-<!--- Describe how your model was evaluated -->
+# Bias, Risks, and Limitations
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+<!-- 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.
 
-## Training
-The model was trained with the parameters:
+## Recommendations
 
-**DataLoader**:
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-`torch.utils.data.dataloader.DataLoader` of length 190 with parameters:
-```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-**Loss**:
+# Training Details
 
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
+## Training Data
 
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 3,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 100,
-    "weight_decay": 0.01
-}
-```
+<!-- This should link to a Data 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]
 
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
-)
-```
+## 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. -->
+
+### Preprocessing
+
+[More Information Needed]
+
+### Speeds, Sizes, Times
+
+<!-- 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 Data 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]
 
-## Citing & Authors
+# Model Card Contact
 
-<!--- Describe where people can find more information -->
+[More Information Needed]