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@@ -7,24 +7,28 @@ widget:
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  - text: Rigtig god service!
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  ---
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- Classification model for short customer reviews written in Danish.
 
 
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  The model uses 5 classes ranging from 1-5 stars:
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- * ⭐ (poor)
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- * ⭐⭐
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- * ⭐⭐⭐
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- * ⭐⭐⭐⭐
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  * ⭐⭐⭐⭐⭐ (very good)
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- The model is fine-tuned using a pretrained [Danish BERT model]("Maltehb/danish-bert-botxo").
 
 
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  Test the model using the [🤗Transformers](https://github.com/huggingface/transformers) library pipeline:
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  ```python
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  from transformers import pipeline
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- classifier = pipeline("sentiment-analysis", model="danish-bert-review-sentiment")
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  classifier("Intet virkede og ingen hjælp at hente.")
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  #[{'label': '⭐', 'score': 0.4953940808773041}]
@@ -35,6 +39,6 @@ Or load it using the Auto* classes:
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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- model = AutoModelForSequenceClassification.from_pretrained("danish-bert-review-sentiment")
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- tokenizer = AutoTokenizer.from_pretrained("danish-bert-review-sentiment")
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  ```
 
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  - text: Rigtig god service!
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  ---
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+ # What is this?
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+
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+ BERT classification model for short customer reviews written in Danish.
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  The model uses 5 classes ranging from 1-5 stars:
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+ * ⭐ (very poor)
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+ * ⭐⭐ (poor)
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+ * ⭐⭐⭐ (neutral)
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+ * ⭐⭐⭐⭐ (good)
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  * ⭐⭐⭐⭐⭐ (very good)
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+ The model is fine-tuned using the pre-trained [Danish BERT model]("Maltehb/danish-bert-botxo").
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+
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+ # How to use
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  Test the model using the [🤗Transformers](https://github.com/huggingface/transformers) library pipeline:
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  ```python
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  from transformers import pipeline
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+ classifier = pipeline("sentiment-analysis", model="KennethTM/danish-bert-review-sentiment")
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  classifier("Intet virkede og ingen hjælp at hente.")
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  #[{'label': '⭐', 'score': 0.4953940808773041}]
 
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ model = AutoModelForSequenceClassification.from_pretrained("KennethTM/danish-bert-review-sentiment")
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+ tokenizer = AutoTokenizer.from_pretrained("KennethTM/danish-bert-review-sentiment")
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  ```