Instructions to use emekaphilians/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emekaphilians/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emekaphilians/model_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emekaphilians/model_output") model = AutoModelForSequenceClassification.from_pretrained("emekaphilians/model_output") - Notebooks
- Google Colab
- Kaggle
model_output
This model is a fine-tuned version of microsoft/codebert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0368
- F1: 0.9892
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: 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 301 | 0.1471 | 0.9576 |
| 0.5648 | 2.0 | 602 | 0.0658 | 0.9822 |
| 0.5648 | 3.0 | 903 | 0.0901 | 0.9784 |
| 0.0460 | 4.0 | 1204 | 0.0639 | 0.9829 |
| 0.0282 | 5.0 | 1505 | 0.0637 | 0.9829 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for emekaphilians/model_output
Base model
microsoft/codebert-base