Instructions to use evelnap/baseline_s1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evelnap/baseline_s1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="evelnap/baseline_s1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("evelnap/baseline_s1") model = AutoModelForSequenceClassification.from_pretrained("evelnap/baseline_s1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
baseline_s1
This model is a fine-tuned version of indolem/indobertweet-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4306
- Accuracy: 0.9141
- F1: 0.3870
- Recall: 0.2979
- Precision: 0.5521
- Roc Auc: 0.6369
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.7387 | 1.0 | 1319 | 0.4296 | 0.9143 | 0.3859 | 0.2958 | 0.5547 | 0.6360 |
| 0.0890 | 2.0 | 2638 | 0.5007 | 0.8910 | 0.4978 | 0.5938 | 0.4286 | 0.7573 |
| 0.1856 | 3.0 | 3957 | 0.5845 | 0.9045 | 0.4312 | 0.3979 | 0.4704 | 0.6765 |
| 0.4235 | 4.0 | 5276 | 0.6608 | 0.9103 | 0.4153 | 0.35 | 0.5106 | 0.6582 |
| 0.2436 | 5.0 | 6595 | 0.7503 | 0.9037 | 0.4466 | 0.4271 | 0.4680 | 0.6892 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for evelnap/baseline_s1
Base model
indolem/indobertweet-base-uncased