Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use artem2284708/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use artem2284708/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="artem2284708/checkpoints")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("artem2284708/checkpoints") model = AutoModelForSequenceClassification.from_pretrained("artem2284708/checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
checkpoints
This model is a fine-tuned version of artem2284708/my_awesome_model on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8750
- Accuracy: 0.7
- F1: 0.6966
- Precision: 0.6986
- Recall: 0.7
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: 16
- 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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.7623 | 1.0 | 22 | 0.9081 | 0.6 | 0.5838 | 0.6351 | 0.6 |
| 0.5637 | 2.0 | 44 | 0.7482 | 0.65 | 0.6415 | 0.6664 | 0.65 |
| 0.3885 | 3.0 | 66 | 0.7059 | 0.7333 | 0.7243 | 0.7542 | 0.7333 |
| 0.2214 | 4.0 | 88 | 0.6494 | 0.7333 | 0.7316 | 0.7458 | 0.7333 |
| 0.1379 | 5.0 | 110 | 0.7677 | 0.7167 | 0.7136 | 0.7217 | 0.7167 |
| 0.0834 | 6.0 | 132 | 0.6474 | 0.7167 | 0.7162 | 0.7167 | 0.7167 |
| 0.0586 | 7.0 | 154 | 0.7048 | 0.75 | 0.7441 | 0.7616 | 0.75 |
| 0.0455 | 8.0 | 176 | 0.6778 | 0.7667 | 0.7646 | 0.7711 | 0.7667 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
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