README.md

metadata

base_model: readerbench/RoBERT-base
language:
  - ro
tags:
  - sentiment
  - classification
  - nlp
  - bert
datasets:
  - decathlon_reviews
  - cinemagia_reviews
metrics:
  - accuracy
  - precision
  - recall
  - f1
  - f1 weighted
model-index:
  - name: ro-sentiment-03
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          type: ro_sent
          name: Rommanian Sentiment Dataset
          config: default
          split: all
        metrics:
          - type: accuracy
            value: 0.85
            name: Accuracy
          - type: precision
            value: 0.85
            name: Precision
          - type: recall
            value: 0.85
            name: Recall
          - type: f1_weighted
            value: 0.85
            name: Weighted F1
          - type: f1_macro
            value: 0.84
            name: Weighted F1
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          type: laroseda
          name: A Large Romanian Sentiment Data Set
          config: default
          split: all
        metrics:
          - type: accuracy
            value: 0.85
            name: Accuracy
          - type: precision
            value: 0.86
            name: Precision
          - type: recall
            value: 0.85
            name: Recall
          - type: f1_weighted
            value: 0.84
            name: Weighted F1
          - type: f1_macro
            value: 0.84
            name: Weighted F1

ro-sentiment-03

This model is a fine-tuned version of readerbench/RoBERT-base on the None 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

Evaluation on other datasets

SENT_RO

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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