metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-uncased-finetuned-resume
results: []
distilbert-base-uncased-finetuned-resume
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: 0.7572
- Accuracy: 0.6121
- Precision: 0.5993
- Recall: 0.5837
- F1: 0.5817
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.961 | 1.0 | 583 | 0.7683 | 0.6508 | 0.6194 | 0.5758 | 0.5509 |
| 0.7218 | 2.0 | 1166 | 0.7392 | 0.6424 | 0.6484 | 0.5936 | 0.5577 |
| 0.6682 | 3.0 | 1749 | 0.7518 | 0.6358 | 0.5780 | 0.6620 | 0.6089 |
| 0.6262 | 4.0 | 2332 | 0.7457 | 0.6199 | 0.5959 | 0.6417 | 0.5964 |
| 0.59 | 5.0 | 2915 | 0.7572 | 0.6121 | 0.5993 | 0.5837 | 0.5817 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1