Instructions to use Michael8952/hf_outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Michael8952/hf_outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Michael8952/hf_outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Michael8952/hf_outputs") model = AutoModelForSequenceClassification.from_pretrained("Michael8952/hf_outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hf_outputs
This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8878
- Accuracy: 0.8542
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7716 | 1.0 | 30 | 0.5556 | 0.725 |
| 0.4529 | 2.0 | 60 | 0.3979 | 0.8333 |
| 0.2811 | 3.0 | 90 | 0.3776 | 0.8625 |
| 0.1563 | 4.0 | 120 | 0.4707 | 0.8583 |
| 0.0984 | 5.0 | 150 | 0.5073 | 0.85 |
| 0.042 | 6.0 | 180 | 0.7112 | 0.8667 |
| 0.0273 | 7.0 | 210 | 0.8339 | 0.8542 |
| 0.0302 | 8.0 | 240 | 0.8313 | 0.8542 |
| 0.0125 | 9.0 | 270 | 0.8709 | 0.8542 |
| 0.0098 | 10.0 | 300 | 0.8878 | 0.8542 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.1.1
- Tokenizers 0.22.1
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Model tree for Michael8952/hf_outputs
Base model
openai-community/gpt2