# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="BatSilver/results")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("BatSilver/results")
model = AutoModelForSequenceClassification.from_pretrained("BatSilver/results")Quick Links
results
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4999
- Accuracy: 0.9091
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 7 | 0.6610 | 0.6364 |
| No log | 2.0 | 14 | 0.6433 | 0.6364 |
| No log | 3.0 | 21 | 0.6237 | 0.6364 |
| No log | 4.0 | 28 | 0.5876 | 0.6364 |
| No log | 5.0 | 35 | 0.5222 | 0.7273 |
| No log | 6.0 | 42 | 0.5004 | 0.7273 |
| No log | 7.0 | 49 | 0.4665 | 0.7273 |
| No log | 8.0 | 56 | 0.4207 | 0.8182 |
| No log | 9.0 | 63 | 0.4235 | 0.9091 |
| No log | 10.0 | 70 | 0.3507 | 0.9091 |
| No log | 11.0 | 77 | 0.3359 | 0.8182 |
| No log | 12.0 | 84 | 0.3253 | 0.9091 |
| No log | 13.0 | 91 | 0.3268 | 0.9091 |
| No log | 14.0 | 98 | 0.3321 | 0.9091 |
| 0.3884 | 15.0 | 105 | 0.3332 | 0.9091 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for BatSilver/results
Base model
aubmindlab/bert-base-arabertv02
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