Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("padmajabfrl/Gender-Classification")
model = AutoModelForSequenceClassification.from_pretrained("padmajabfrl/Gender-Classification")Quick Links
Gender-Classification
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0035 | 1.0 | 4390 | 0.0004 | 1.0000 |
| 0.0005 | 2.0 | 8780 | 0.0002 | 1.0000 |
| 0.0 | 3.0 | 13170 | 0.0000 | 1.0 |
| 0.0 | 4.0 | 17560 | 0.0000 | 1.0 |
| 0.0 | 5.0 | 21950 | 0.0000 | 1.0 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="padmajabfrl/Gender-Classification")