Update README.md

README.md

@@ -1,3 +1,74 @@
 ---
+language: da
+tags:
+- danish
+- bert
+- sentiment
+- text-classification
+- Maltehb/danish-bert-botxo
+- Helsinki-NLP/opus-mt-en-da
+- go-emotion
+- Certainly
 license: cc-by-4.0
+datasets:
+- go_emotions
+metrics:
+- Accuracy
+widget:
+- text: "Det er så sødt af dig at tænke på andre på den måde ved du det?"
+- text: "Jeg vil gerne have en playstation."
+- text: "Jeg elsker dig"
 ---
+
+# Danish-Bert-GoÆmotion
+
+Danish Go-Emotion classifier. [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) (uncased) finetuned on a translation of the [go_emotion](https://huggingface.co/datasets/go_emotions) dataset using [Helsinki-NLP/opus-mt-en-da](https://huggingface.co/Helsinki-NLP/opus-mt-de-en). Thus,performance is obviousely only as good as the translation model.
+
+
+## Training Parameters:
+
+```
+Num examples = 189900
+Num Epochs = 3
+Train batch = 8
+Eval batch = 8
+Learning Rate = 3e-5
+Warmup steps = 4273
+Total optimization steps = 71125
+```
+
+## Loss
+### Training loss
+![](wb_loss.png)
+
+### Eval. loss
+```
+0.1178 (21100 examples)
+```
+
+
+## Using the model with `transformers`
+Easiest use with `transformers` and `pipeline`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+model = AutoModelForSequenceClassification.from_pretrained('RJuro/danish-bert-go-aemotion')
+tokenizer = AutoTokenizer.from_pretrained('RJuro/danish-bert-go-aemotion')
+
+classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+classifier('jeg elsker dig')
+```
+
+`[{'label': 'kærlighed', 'score': 0.9634820818901062}]`
+
+## Using the model with `simpletransformers`
+
+```python
+from simpletransformers.classification import MultiLabelClassificationModel
+
+model = MultiLabelClassificationModel('bert', 'RJuro/danish-bert-go-aemotion')
+
+predictions, raw_outputs = model.predict(df['text'])
+```