metadata
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: roberta-large
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-token-classification
results: []
bert-large-token-classification
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2157
- Precision: 0.4271
- Recall: 0.5155
- F1: 0.4671
- Accuracy: 0.9481
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: 0.001
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3642 | 1.0 | 741 | 0.2888 | 0.2211 | 0.2315 | 0.2262 | 0.9264 |
| 0.2508 | 2.0 | 1482 | 0.2862 | 0.3301 | 0.3645 | 0.3465 | 0.9217 |
| 0.1609 | 3.0 | 2223 | 0.2247 | 0.3109 | 0.4309 | 0.3612 | 0.9411 |
| 0.1404 | 4.0 | 2964 | 0.2391 | 0.3563 | 0.4669 | 0.4042 | 0.9303 |
| 0.0937 | 5.0 | 3705 | 0.2157 | 0.4271 | 0.5155 | 0.4671 | 0.9481 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1