metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: WITHINAPPS_NDD-petclinic_test-content-CWAdj
results: []
WITHINAPPS_NDD-petclinic_test-content-CWAdj
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: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 69 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 2.0 | 138 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 3.0 | 207 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 4.0 | 276 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 5.0 | 345 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1