Instructions to use johnny8808/test-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johnny8808/test-trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="johnny8808/test-trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("johnny8808/test-trainer") model = AutoModelForSequenceClassification.from_pretrained("johnny8808/test-trainer") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: test-trainer
results: []
test-trainer
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6436
- Accuracy: 0.8382
- F1: 0.8846
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: Use 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 459 | 0.4523 | 0.8309 | 0.8761 |
| 0.5648 | 2.0 | 918 | 0.4340 | 0.8284 | 0.8754 |
| 0.3674 | 3.0 | 1377 | 0.6436 | 0.8382 | 0.8846 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.9.0.dev20250812+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1