Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Phiby/superwomen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Phiby/superwomen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Phiby/superwomen")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Phiby/superwomen") model = AutoModelForSequenceClassification.from_pretrained("Phiby/superwomen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
superwomen
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: 0.4064
- Accuracy: 0.942
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 469 | 2.7836 | 0.778 |
| 3.9815 | 2.0 | 938 | 1.2347 | 0.898 |
| 1.875 | 3.0 | 1407 | 0.6481 | 0.9273 |
| 0.8377 | 4.0 | 1876 | 0.4547 | 0.9393 |
| 0.4736 | 5.0 | 2345 | 0.4064 | 0.942 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for Phiby/superwomen
Base model
distilbert/distilbert-base-uncased