# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("TobennaUdeze/text_classification_hw6")
model = AutoModelForSequenceClassification.from_pretrained("TobennaUdeze/text_classification_hw6")Quick Links
text_classification_hw6
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2250
- Accuracy: 0.9430
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.218 | 1.0 | 1563 | 0.1729 | 0.9366 |
| 0.1552 | 2.0 | 3126 | 0.2250 | 0.9430 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for TobennaUdeze/text_classification_hw6
Base model
albert/albert-base-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TobennaUdeze/text_classification_hw6")