Instructions to use AleRams/app_prova with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AleRams/app_prova with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AleRams/app_prova")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AleRams/app_prova") model = AutoModelForSequenceClassification.from_pretrained("AleRams/app_prova", device_map="auto") - Notebooks
- Google Colab
- Kaggle
app_prova
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: 1.1297
- Accuracy: 0.69
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1788 | 0.4 | 50 | 1.0801 | 0.635 |
| 0.9743 | 0.8 | 100 | 1.1113 | 0.686 |
| 0.8909 | 1.2 | 150 | 1.0996 | 0.698 |
| 0.7816 | 1.6 | 200 | 1.0869 | 0.695 |
| 0.6782 | 2.0 | 250 | 1.0705 | 0.685 |
| 0.693 | 2.4 | 300 | 1.1389 | 0.678 |
| 0.4672 | 2.8 | 350 | 1.1297 | 0.69 |
Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for AleRams/app_prova
Base model
google-bert/bert-base-uncased