---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentiment-polish-gpt2-small
  results:
  - task:
      type: text-classification
    dataset:
      type: allegro/klej-polemo2-out
      name: klej-polemo2-out
    metrics:
      - type: accuracy
        value: 98.38%
license: mit
language:
- pl
datasets:
- clarin-pl/polemo2-official
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# sentiment-polish-gpt2-small

This model was trained from [polish-gpt2-small](https://huggingface.co./sdadas/polish-gpt2-small) on the [polemo2-official](https://huggingface.co./datasets/clarin-pl/polemo2-official) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4659
- Accuracy: 0.9627

## Model description

Trained from [polish-gpt2-small](https://huggingface.co./sdadas/polish-gpt2-small)

## Intended uses & limitations

Sentiment analysis - neutral/negative/positive/ambiguous

## Training and evaluation data
Merged all rows from [polemo2-official](https://huggingface.co./datasets/clarin-pl/polemo2-official) dataset.

Train/test split: 80%/20%

Datacollator:
```py
from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(
  tokenizer=tokenizer,
  padding="longest",
  max_length=128,
  pad_to_multiple_of=8
)
```

## Training procedure

GPU: RTX 3090

Training time: 2:53:05

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4049        | 1.0   | 3284  | 0.3351          | 0.8792   |
| 0.1885        | 2.0   | 6568  | 0.2625          | 0.9218   |
| 0.1182        | 3.0   | 9852  | 0.2583          | 0.9419   |
| 0.0825        | 4.0   | 13136 | 0.2886          | 0.9482   |
| 0.0586        | 5.0   | 16420 | 0.3343          | 0.9538   |
| 0.034         | 6.0   | 19704 | 0.3734          | 0.9595   |
| 0.0288        | 7.0   | 22988 | 0.4125          | 0.9599   |
| 0.0185        | 8.0   | 26273 | 0.4262          | 0.9626   |
| 0.0069        | 9.0   | 29557 | 0.4529          | 0.9622   |
| 0.0059        | 10.0  | 32840 | 0.4659          | 0.9627   |

### Evaluation

Evaluated on [allegro/klej-polemo2-out](https://huggingface.co./datasets/allegro/klej-polemo2-out) test dataset.
```py
from datasets import load_dataset
from evaluate import evaluator

data = load_dataset("allegro/klej-polemo2-out", split="test").shuffle(seed=42)
task_evaluator = evaluator("text-classification")

# fix labels
l = {
        "__label__meta_zero": 0,
        "__label__meta_minus_m": 1,
        "__label__meta_plus_m": 2,
        "__label__meta_amb": 3
    }
def fix_labels(examples):
    examples["target"] = l[examples["target"]]
    return examples
data = data.map(fix_labels)

eval_resutls = task_evaluator.compute(
    model_or_pipeline="nie3e/sentiment-polish-gpt2-small",
    data=data,
    label_mapping={"NEUTRAL": 0, "NEGATIVE": 1, "POSITIVE": 2, "AMBIGUOUS": 3},
    input_column="sentence",
    label_column="target"
)

print(eval_resutls)
```

```json
{
    "accuracy": 0.9838056680161943,
    "total_time_in_seconds": 5.2441766999982065,
    "samples_per_second": 94.1997244296076,
    "latency_in_seconds": 0.010615742307688678
}
```

### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0