Instructions to use id66pj/runs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use id66pj/runs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="id66pj/runs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("id66pj/runs") model = AutoModelForSequenceClassification.from_pretrained("id66pj/runs") - Notebooks
- Google Colab
- Kaggle
runs
This model is a fine-tuned version of AI-Growth-Lab/PatentSBERTa on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6249
- Accuracy: 0.7
- F1: 0.8235
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
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 6 | 0.6250 | 0.7 | 0.8235 |
| No log | 2.0 | 12 | 0.6200 | 0.7 | 0.8235 |
Framework versions
- Transformers 5.2.0
- Pytorch 2.10.0+cu128
- Datasets 4.6.0
- Tokenizers 0.22.2
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Model tree for id66pj/runs
Base model
AI-Growth-Lab/PatentSBERTa