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/Ethnicity-Classification")
model = AutoModelForSequenceClassification.from_pretrained("padmajabfrl/Ethnicity-Classification")Quick Links
Ethnicity-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: 0.0358
- Accuracy: 0.9951
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 |
|---|---|---|---|---|
| 0.0569 | 1.0 | 5305 | 0.0597 | 0.9884 |
| 0.0324 | 2.0 | 10610 | 0.0418 | 0.9924 |
| 0.0151 | 3.0 | 15915 | 0.0359 | 0.9941 |
| 0.0037 | 4.0 | 21220 | 0.0366 | 0.9946 |
| 0.0044 | 5.0 | 26525 | 0.0358 | 0.9951 |
Framework versions
- Transformers 4.14.1
- Pytorch 1.12.0
- Datasets 2.9.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="padmajabfrl/Ethnicity-Classification")