Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ThirstBloody/students_scores_model_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThirstBloody/students_scores_model_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ThirstBloody/students_scores_model_4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ThirstBloody/students_scores_model_4") model = AutoModelForSequenceClassification.from_pretrained("ThirstBloody/students_scores_model_4") - Notebooks
- Google Colab
- Kaggle
students_scores_model_4
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0314
- F1: 0.5793
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: 16
- seed: 42
- optimizer: Use 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 282 | 1.0314 | 0.5793 |
| 0.7598 | 2.0 | 564 | 1.0314 | 0.5793 |
| 0.7598 | 3.0 | 846 | 1.0314 | 0.5793 |
| 0.8163 | 4.0 | 1128 | 1.0314 | 0.5793 |
| 0.8163 | 5.0 | 1410 | 1.0314 | 0.5793 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for ThirstBloody/students_scores_model_4
Base model
distilbert/distilbert-base-uncased