Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use PES1UG22CS292/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PES1UG22CS292/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PES1UG22CS292/my_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PES1UG22CS292/my_model") model = AutoModelForSequenceClassification.from_pretrained("PES1UG22CS292/my_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3632
- Accuracy: 0.9323
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2276 | 1.0 | 1563 | 0.2284 | 0.9116 |
| 0.1591 | 2.0 | 3126 | 0.2506 | 0.9248 |
| 0.0944 | 3.0 | 4689 | 0.3450 | 0.9224 |
| 0.0506 | 4.0 | 6252 | 0.3599 | 0.9276 |
| 0.0322 | 5.0 | 7815 | 0.3632 | 0.9323 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.0+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for PES1UG22CS292/my_model
Base model
distilbert/distilbert-base-uncased