Instructions to use PawanJain409/news20normal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PawanJain409/news20normal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PawanJain409/news20normal")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PawanJain409/news20normal") model = AutoModelForSequenceClassification.from_pretrained("PawanJain409/news20normal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
news20normal
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3255
- Accuracy: 0.7258
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: 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 |
|---|---|---|---|---|
| 1.2768 | 1.0 | 694 | 1.0235 | 0.6942 |
| 0.6319 | 2.0 | 1388 | 0.9923 | 0.7118 |
| 0.3464 | 3.0 | 2082 | 1.0939 | 0.7196 |
| 0.172 | 4.0 | 2776 | 1.2470 | 0.7249 |
| 0.0846 | 5.0 | 3470 | 1.3255 | 0.7258 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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Model tree for PawanJain409/news20normal
Base model
google-bert/bert-base-uncased