| ---
|
| library_name: peft
|
| base_model: DeepPavlov/rubert-base-cased
|
| tags:
|
| - generated_from_trainer
|
| metrics:
|
| - precision
|
| - recall
|
| - f1
|
| model-index:
|
| - name: pollen-ner-2000
|
| results: []
|
| ---
|
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # pollen-ner-2000 |
|
|
| This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1411 |
| - Precision: 0.8936 |
| - Recall: 0.9277 |
| - F1: 0.9103 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.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: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
| | No log | 1.0 | 250 | 0.1378 | 0.8859 | 0.9197 | 0.9025 | |
| | 0.15 | 2.0 | 500 | 0.1402 | 0.8917 | 0.9257 | 0.9084 | |
| | 0.15 | 3.0 | 750 | 0.1395 | 0.8793 | 0.9217 | 0.9 | |
| | 0.1469 | 4.0 | 1000 | 0.1411 | 0.8936 | 0.9277 | 0.9103 | |
| | 0.1469 | 5.0 | 1250 | 0.1420 | 0.8889 | 0.9317 | 0.9098 | |
| | 0.1463 | 6.0 | 1500 | 0.1429 | 0.8836 | 0.9297 | 0.9061 | |
|
|
|
|
| ### Framework versions |
|
|
| - PEFT 0.15.2 |
| - Transformers 4.51.3 |
| - Pytorch 2.7.0+cu128 |
| - Datasets 3.5.0 |
| - Tokenizers 0.21.1 |
