This model is a fine-tuned version of readerbench/RoBERT-base on the Decathlon reviews and Cinemagia reviews dataset. It achieves the following results on the evaluation set:

Loss: 0.3923
Accuracy: 0.8307
Precision: 0.8366
Recall: 0.8959
F1: 0.8652
F1 Weighted: 0.8287

Output labels:

LABEL_0 = Negative Sentiment
LABEL_1 = Positive Sentiment

Evaluation on other datasets

SENT_RO

	precision	recall	f1-score	support
Negative (0)	0.79	0.83	0.81	11,675
Positive (1)	0.88	0.85	0.87	17,271

Accuracy			0.85	28,946
Macro Avg	0.84	0.84	0.84	28,946
Weighted Avg	0.85	0.85	0.85	28,946

LaRoSeDa

	precision	recall	f1-score	support
Negative (0)	0.79	0.94	0.86	7,500
Positive (1)	0.93	0.75	0.83	7,500

Accuracy			0.85	15,000
Macro Avg	0.86	0.85	0.84	15,000
Weighted Avg	0.86	0.85	0.84	15,000

Model description

Finetuned Romanian BERT model for sentiment classification.

Trained on a mix of product reviews from Decathlon retailer website and movie reviews from cinemagia.

Intended uses & limitations

Sentiment classification for Romanian Language.

Biased towards Product reviews.

There is no "neutral" sentiment label.

Training and evaluation data

Trained on:

Decathlon Dataset available on request
Cinemagia Movie reviews public on kaggle Link

Evaluated on

Holdout data from training dataset
RO_SENT Dataset
LaROSeDa Dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 64
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.2
num_epochs: 10 (Early stop epoch 3, best epoch 2)

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	F1 Weighted
0.4198	1.0	1629	0.3983	0.8377	0.8791	0.8721	0.8756	0.8380
0.3861	2.0	3258	0.4312	0.8429	0.8963	0.8665	0.8812	0.8442
0.3189	3.0	4887	0.3923	0.8307	0.8366	0.8959	0.8652	0.8287

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.3
Tokenizers 0.13.3

readerbench/ro-sentiment

RO-Sentiment