Instructions to use SimoneJLaudani/trainer5c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SimoneJLaudani/trainer5c with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SimoneJLaudani/trainer5c")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SimoneJLaudani/trainer5c") model = AutoModelForSequenceClassification.from_pretrained("SimoneJLaudani/trainer5c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
trainer5c
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: 0.0417
- Accuracy: 1.0
- F1: 1.0
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.3346 | 1.2 | 30 | 0.9342 | 0.5952 | 0.5339 |
| 0.2414 | 2.4 | 60 | 0.8641 | 0.5714 | 0.5215 |
| 0.2536 | 3.6 | 90 | 0.9935 | 0.6429 | 0.6153 |
| 0.1149 | 4.8 | 120 | 0.3131 | 0.9048 | 0.8970 |
| 0.0487 | 6.0 | 150 | 0.2916 | 0.8571 | 0.8095 |
| 0.0188 | 7.2 | 180 | 0.0858 | 1.0 | 1.0 |
| 0.0108 | 8.4 | 210 | 0.0473 | 1.0 | 1.0 |
| 0.0093 | 9.6 | 240 | 0.0433 | 1.0 | 1.0 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for SimoneJLaudani/trainer5c
Base model
google-bert/bert-base-uncased