---
license: cc-by-sa-4.0
datasets:
- cjvt/sentinews
language:
- sl
library_name: transformers
pipeline_tag: text-classification
model-index:
  - name: sloberta-sentinews-sentence
    results:
    - task:
        type: text-classification
        name: Sentiment classification
      dataset:
        type: cjvt/sentinews
        name: SentiNews
        config: sentence_level
      metrics:
        - type: f1
          value: 0.6851357247321056
          name: Test macro F1
        - type: accuracy
          value: 0.7158081705150977
          name: Test accuracy
        - type: f1
          value: 0.6934678744913757
          name: Validation macro F1
        - type: accuracy
          value: 0.7207815275310835
          name: Validation accuracy 
---

# sloberta-sentinews-sentence

Slovenian 3-class sentiment classifier - [SloBERTa](https://huggingface.co./EMBEDDIA/sloberta) fine-tuned on the sentence-level config of the 
SentiNews dataset.

The model is intended as:  
(1) an out-of-the box sentence-level sentiment classifier or   
(2) a sentence-level sentiment classification baseline.

## Fine-tuning details
The model was fine-tuned on a random 90%/5%/5% train-val-test split of the `sentence_level` configuration of the [cjvt/sentinews](https://huggingface.co./datasets/cjvt/sentinews) dataset 
using the following hyperparameters:  
```
max_length = 79  # 99th percentile of encoded training sequences, sequences are padded/truncated to this length
batch_size = 128
optimizer = "adamw_torch"
learning_rate = 2e-5
num_epochs = 10
validation_metric = "macro_f1"
```

Feel free to inspect `training_args.bin` for more details.

If you wish to directly compare your model to this one, you should use the same split as this model. To do so, use the following code:  
```python
import json
import datasets

# You can find split_indices.json in the 'Files and versions' tab 
with open("split_indices.json", "r") as f_split:
  split = json.load(f_split)

data = datasets.load_dataset("cjvt/sentinews", "sentence_level", split="train")
train_data = data.select(split["train_indices"])
dev_data = data.select(split["dev_indices"])
test_data = data.select(split["test_indices"])
```

## Evaluation results

Best validation set results:
```
{
  "eval_accuracy": 0.7207815275310835,
  "eval_f1_macro": 0.6934678744913757,
  "eval_f1_negative": 0.7042136003337507,
  "eval_f1_neutral": 0.759215853398679,
  "eval_f1_positive": 0.6169741697416974,
  "eval_loss": 0.6337869167327881,
  "eval_precision_negative": 0.6685148514851486,
  "eval_precision_neutral": 0.7752393385552655,
  "eval_precision_positive": 0.6314199395770392,
  "eval_recall_negative": 0.74394006170119,
  "eval_recall_neutral": 0.7438413361169103,
  "eval_recall_positive": 0.6031746031746031
}
```

Test set results:
```
{
  "test_loss": 0.6395984888076782,
  "test_accuracy": 0.7158081705150977,
  "test_precision_negative": 0.6570397111913358,
  "test_recall_negative": 0.7292965271593945,
  "test_f1_negative": 0.6912850812407682,
  "test_precision_neutral": 0.7748017998714377,
  "test_recall_neutral": 0.7418957734919983,
  "test_f1_neutral": 0.7579918247563149,
  "test_precision_positive": 0.6155642023346304,
  "test_recall_positive": 0.5969811320754717,
  "test_f1_positive": 0.6061302681992337,
  "test_f1_macro": 0.6851357247321056,
}
```