metadata
library_name: peft
license: cc-by-4.0
base_model: pczarnik/herbert-base-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: herbert-ner-lora-core
results: []
herbert-ner-lora-core
This model is a fine-tuned version of pczarnik/herbert-base-ner on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0054
- Precision: 0.9028
- Recall: 0.9178
- F1: 0.9102
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| No log | 1.0 | 488 | 0.0136 | 0.7914 | 0.8605 | 0.8245 |
| 0.1553 | 2.0 | 976 | 0.0082 | 0.8759 | 0.9086 | 0.8920 |
| 0.0144 | 3.0 | 1464 | 0.0064 | 0.8916 | 0.9153 | 0.9033 |
| 0.0108 | 4.0 | 1952 | 0.0056 | 0.8973 | 0.9145 | 0.9058 |
| 0.0094 | 5.0 | 2440 | 0.0054 | 0.9028 | 0.9178 | 0.9102 |
Framework versions
- PEFT 0.12.0
- Transformers 4.50.3
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.21.1