Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Anurag0961/try-out-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anurag0961/try-out-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Anurag0961/try-out-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Anurag0961/try-out-model") model = AutoModelForSequenceClassification.from_pretrained("Anurag0961/try-out-model") - Notebooks
- Google Colab
- Kaggle
try-out-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: 1.0210
- F1: 0.8037
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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 3.1006 | 1.0 | 188 | 2.4095 | 0.4504 |
| 1.9221 | 2.0 | 376 | 1.5023 | 0.7072 |
| 1.2765 | 3.0 | 564 | 1.1263 | 0.7895 |
| 0.9936 | 4.0 | 752 | 1.0210 | 0.8037 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
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