Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Oscar9898/kartik-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oscar9898/kartik-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Oscar9898/kartik-finetune")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Oscar9898/kartik-finetune") model = AutoModelForSequenceClassification.from_pretrained("Oscar9898/kartik-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
kartik-finetune
This model is a fine-tuned version of karths/binary_classification_train_TD on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6296
- Accuracy: 0.6840
- Precision: 0.7261
- Recall: 0.7553
- F1: 0.7404
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.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: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6222 | 1.0 | 968 | 0.6143 | 0.6683 | 0.7021 | 0.7712 | 0.7351 |
| 0.5823 | 2.0 | 1936 | 0.6121 | 0.6763 | 0.7202 | 0.7482 | 0.7339 |
| 0.5491 | 3.0 | 2904 | 0.6084 | 0.6844 | 0.7078 | 0.8022 | 0.7520 |
| 0.5256 | 4.0 | 3872 | 0.6296 | 0.6840 | 0.7261 | 0.7553 | 0.7404 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.7.1+cu118
- Datasets 4.0.0
- Tokenizers 0.21.1
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Model tree for Oscar9898/kartik-finetune
Base model
karths/binary_classification_train_TD