Instructions to use lenndi/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lenndi/model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lenndi/model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lenndi/model_output") model = AutoModelForSequenceClassification.from_pretrained("lenndi/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
model_output
This model is a fine-tuned version of beomi/kcbert-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1352
- Lrap: 0.8748
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: 128
- eval_batch_size: 128
- 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 | Lrap |
|---|---|---|---|---|
| No log | 1.0 | 118 | 0.1415 | 0.8688 |
| No log | 2.0 | 236 | 0.1352 | 0.8748 |
Framework versions
- Transformers 5.10.2
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for lenndi/model_output
Base model
beomi/kcbert-base