|
|
--- |
|
|
license: cc-by-nc-4.0 |
|
|
language: |
|
|
- is |
|
|
pipeline_tag: text-classification |
|
|
library_name: transformers |
|
|
tags: |
|
|
- icelandic |
|
|
- sentiment-analysis |
|
|
- text-classification |
|
|
- sequence-classification |
|
|
- social-media |
|
|
sources: |
|
|
Risamálheildin slices of forums/blogs, manually labelled by us, and our own |
|
|
small corpus made from samples gathered from social media |
|
|
--- |
|
|
|
|
|
|
|
|
**Task**: 3-class sentiment analysis → `["negative", "neutral", "positive"]` |
|
|
**Base model**: `mideind/IceBERT-igc` (Icelandic RoBERTa) |
|
|
|
|
|
## TL;DR |
|
|
|
|
|
A small Icelandic RoBERTa fine-tuned for 3-way sentiment on non-ironic text. Pairs well **after** an irony gate (first run the irony model; only classify sentiment if `not_ironic`). |
|
|
|
|
|
--- |
|
|
|
|
|
## How to use |
|
|
|
|
|
```python |
|
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
|
|
|
|
model_id = "ambj24/icelandic-sentiment" |
|
|
tok = AutoTokenizer.from_pretrained(model_id) |
|
|
mod = AutoModelForSequenceClassification.from_pretrained(model_id) |
|
|
|
|
|
text = "Þjónustan var frábær!" |
|
|
inputs = tok(text, return_tensors="pt") |
|
|
probs = mod(**inputs).logits.softmax(-1).tolist()[0] |
|
|
|
|
|
labels = ["negative", "neutral", "positive"] |
|
|
print(dict(zip(labels, probs))) |
|
|
|
|
|
Input length: short posts; trained with max length ~128 tokens. |
|
|
|
|
|
Data: social-media style Icelandic. |
|
|
Domain shift: trained on short, informal posts. |
|
|
|
|
|
Positive/neutral/negative labels; only examples judged not ironic. |
|
|
|
|
|
Typical setup: 3 epochs, LR ≈ 2e-5, batch ≈ 16, max length 128. |
