Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Yongce/my_awesome_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yongce/my_awesome_model_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yongce/my_awesome_model_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Yongce/my_awesome_model_2") model = AutoModelForSequenceClassification.from_pretrained("Yongce/my_awesome_model_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_awesome_model_2
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6156
- Accuracy: 0.8068
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.944 | 1.0 | 740 | 0.5934 | 0.7932 |
| 0.5943 | 2.0 | 1480 | 0.5769 | 0.7977 |
| 0.4412 | 3.0 | 2220 | 0.5824 | 0.8023 |
| 0.3795 | 4.0 | 2960 | 0.5896 | 0.8068 |
| 0.2985 | 5.0 | 3700 | 0.6156 | 0.8068 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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