File size: 2,079 Bytes
f14fc3a c63da06 f14fc3a c63da06 1464bde c63da06 5afc8f1 f14fc3a c63da06 0e0c98c 1464bde c63da06 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
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:
- GoEmotions
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"
- text: "Hvordan håndterer jeg min irriterende nabo?"
---
# 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
- Translating the training data with MT: [Notebook](https://colab.research.google.com/github/RJuro/Da-HyggeBERT-finetuning/blob/main/HyggeBERT_translation_en_da.ipynb)
## 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
### 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'])
``` |