metadata

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

sentiment-polish-gpt2-small

This model was trained from polish-gpt2-small on the 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

Intended uses & limitations

Sentiment analysis - neutral/negative/positive/ambiguous

Training and evaluation data

Merged all rows from polemo2-official dataset.

Train/test split: 80%/20%

Datacollator:

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 test dataset.

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)

{
    "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