Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use qwekuaryee/test_trainer_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qwekuaryee/test_trainer_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qwekuaryee/test_trainer_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("qwekuaryee/test_trainer_2") model = AutoModelForSequenceClassification.from_pretrained("qwekuaryee/test_trainer_2") - Notebooks
- Google Colab
- Kaggle
test_trainer_2
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: 0.5882
- Accuracy: 0.7805
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: 5e-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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7323 | 0.5 | 500 | 0.6435 | 0.7375 |
| 0.6303 | 1.0 | 1000 | 0.5711 | 0.768 |
| 0.4719 | 1.5 | 1500 | 0.6429 | 0.7735 |
| 0.4581 | 2.0 | 2000 | 0.5882 | 0.7805 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for qwekuaryee/test_trainer_2
Base model
distilbert/distilbert-base-uncased