Instructions to use SharonTudi/DIALOGUE_overfit_check_fold_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SharonTudi/DIALOGUE_overfit_check_fold_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SharonTudi/DIALOGUE_overfit_check_fold_4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SharonTudi/DIALOGUE_overfit_check_fold_4") model = AutoModelForSequenceClassification.from_pretrained("SharonTudi/DIALOGUE_overfit_check_fold_4") - Notebooks
- Google Colab
- Kaggle
DIALOGUE_overfit_check_fold_4
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: 0.1481
- Precision: 0.9737
- Recall: 0.9773
- F1: 0.9742
- Accuracy: 0.9737
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: 0.00024
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.4533 | 0.77 | 30 | 0.5054 | 0.9059 | 0.8777 | 0.8782 | 0.8816 |
| 0.4715 | 1.54 | 60 | 0.3453 | 0.9618 | 0.9641 | 0.9619 | 0.9605 |
| 0.268 | 2.31 | 90 | 0.1294 | 0.9625 | 0.9659 | 0.9614 | 0.9605 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for SharonTudi/DIALOGUE_overfit_check_fold_4
Base model
distilbert/distilbert-base-uncased