update model card README.md

README.md

@@ -1,73 +1,49 @@
 ---
-language: de
-datasets:
-- Short-Answer-Feedback/saf_legal_domain_german
 tags:
 - generated_from_trainer
-widget:
-- text: "Antwort: Kommt man einer besoderen Aufforderung nach und zeigt den Unfall unverzüglich an, besteht Versicherungsschutz falls ein Unfall eintritt. Lösung: Merkblatt für Arbeitslose, S. 77: Als Bezieher von Arbeitslosengeld sind Sie gegen Unfall versichert, während Sie einer besonderen Aufforderung nachkommen, eine Agentur für Arbeit oder andere Stelle aufzusuchen (z. B. zur ärztlichen Untersuchung, Vorstellung beim Arbeitgeber, Eingliederungsmaßnahme). Einen Unfall müssen Sie sofort Ihrer Agentur für Arbeit anzeigen. Frage: Inwieweit sind Sie während des Bezugs von Arbeitslosengeld gegen einen Unfall versichert und was sollten Sie nach einem Unfall tun?"
+model-index:
+- name: mbart-score-finetuned-saf-legal-domain
+  results: []
 ---
 
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
 # mbart-score-finetuned-saf-legal-domain
 
-This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german) dataset for Short Answer Feedback (SAF).
+This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset.
 
 ## Model description
 
-This model was built on top of [mBART](https://arxiv.org/abs/2001.08210), which is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages.
-
-It expects inputs in the following format:
-```
-Antwort: [answer] Lösung: [reference_answer] Frage: [question]
-```
-
-In the example above, `[answer]`, `[reference_answer]` and `[question]` should be replaced by the provided answer, the reference answer and the question to which they refer, respectively.
-
-
-The outputs are formatted as follows:
-```
-[score] Feedback: [feedback]
-```
-
-Hence, `[score]` will be a numeric value between 0 and 1, while `[feedback]` will be the textual feedback generated by the model according to the given answer.
+More information needed
 
 ## Intended uses & limitations
 
-This model is intended to be used for Short Answer Feedback generation in the domain of the German social law. Thus, it is not expected to have particularly good performance on sets of questions and answers out of this scope.
-
-It is important to acknowledge that the model underperforms when a question that was not seen during training is given as input for inference. In particular, it tends to classify most answers as being correct and does not provide relevant feedback in such cases. Nevertheless, this limitation could be partially overcome by extending the dataset with the desired question (and associated answers) and fine-tuning it for a few epochs on the new data.
+More information needed
 
 ## Training and evaluation data
 
-As mentioned previously, the model was trained on the [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german) dataset, which is divided into the following splits.
-
-| Split                 | Number of examples |
-| --------------------- | ------------------ |
-| train                 | 1596	             |
-| validation            | 400	             |
-| test_unseen_answers   | 221	             |
-| test_unseen_questions | 275                |
-
-Evaluation was performed on the `test_unseen_answers` and `test_unseen_questions` splits.
+More information needed
 
 ## Training procedure
 
-The [Trainer API](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainer) was used to fine-tune the model. The code utilized for pre-processing and training was mostly adapted from the [summarization script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) made available by HuggingFace.
-
-Training was completed in a little over 1 hour on a GPU on Google Colab.
-
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- num_epochs: 10
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- learning_rate: 5e-05
-- lr_scheduler_type: linear
+- learning_rate: 6e-05
 - train_batch_size: 1
-- gradient_accumulation_steps: 4
 - eval_batch_size: 4
-- mixed_precision_training: Native AMP
 - seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 9
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
 
 ### Framework versions
 
@@ -75,43 +51,3 @@ The following hyperparameters were used during training:
 - Pytorch 1.13.1+cu116
 - Datasets 2.9.0
 - Tokenizers 0.13.2
-
-## Evaluation results
-
-The generated feedback was evaluated through means of the [SacreBLEU](https://huggingface.co/spaces/evaluate-metric/sacrebleu), [ROUGE-2](https://huggingface.co/spaces/evaluate-metric/rouge), [METEOR](https://huggingface.co/spaces/evaluate-metric/meteor), [BERTScore](https://huggingface.co/spaces/evaluate-metric/bertscore) metrics from HuggingFace, while the [Root Mean Squared Error](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error) loss from scikit-learn was used for evaluation of the predicted scores in relation to the golden label scores.
-
-The following results were achieved.
-
-| Split                 | SacreBLEU | ROUGE-2 | METEOR | BERTScore | RMSE  |
-| --------------------- | :-------: | :-----: | :----: | :-------: | :---: |
-| test_unseen_answers   | 33.7	    | 37.2    | 50.7   | 45.0      | 0.264 |
-| test_unseen_questions | 2.9       | 5.7     | 17.0   | 10.8      | 0.331 |
-
-The script used to compute these metrics and perform evaluation can be found in the `evaluation.py` file in this repository.
-
-## Usage
-
-The example below shows how the model can be applied to generate feedback to a given answer.
-
-```python
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-model = AutoModelForSeq2SeqLM.from_pretrained('Short-Answer-Feedback/mbart-score-finetuned-saf-legal-domain')
-tokenizer = AutoTokenizer.from_pretrained('Short-Answer-Feedback/mbart-score-finetuned-saf-legal-domain')
-
-example_input = 'Antwort: Kommt man einer besoderen Aufforderung nach und zeigt den Unfall unverzüglich an, besteht Versicherungsschutz falls ein Unfall eintritt. Lösung: Merkblatt für Arbeitslose, S. 77: Als Bezieher von Arbeitslosengeld sind Sie gegen Unfall versichert, während Sie einer besonderen Aufforderung nachkommen, eine Agentur für Arbeit oder andere Stelle aufzusuchen (z. B. zur ärztlichen Untersuchung, Vorstellung beim Arbeitgeber, Eingliederungsmaßnahme). Einen Unfall müssen Sie sofort Ihrer Agentur für Arbeit anzeigen. Frage: Inwieweit sind Sie während des Bezugs von Arbeitslosengeld gegen einen Unfall versichert und was sollten Sie nach einem Unfall tun?'
-inputs = tokenizer(example_input, max_length=256, padding='max_length', truncation=True, return_tensors='pt')
-
-generated_tokens = model.generate(
-                inputs['input_ids'],
-                attention_mask=inputs['attention_mask'],
-                max_length=128
-            )
-output = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-```
-
-The output produced by the model then looks as follows:
-
-```
-0.875 Feedback: Ihre Antwort ist richtig. Bitte beachten Sie, dass diese Aufforderung sowohl einen Termin bei der Agentur für Arbeit als auch die Vorstellung bei der Vorstellung bei der Agentur für Arbeit beinhalten kann - beispielsweise bei einer Vorstellung bei einer anderen Stelle. Bitte melden Sie einen entsprechenden Unfall sofort Ihrer Agentur für Arbeit.
-```