Instructions to use DanielNRU/pollen-ner-1800 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DanielNRU/pollen-ner-1800 with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("DeepPavlov/rubert-base-cased") model = PeftModel.from_pretrained(base_model, "DanielNRU/pollen-ner-1800") - Notebooks
- Google Colab
- Kaggle
pollen-ner-1800
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1431
- Precision: 0.8783
- Recall: 0.9277
- F1: 0.9023
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 | 225 | 0.1445 | 0.8760 | 0.9217 | 0.8982 |
| No log | 2.0 | 450 | 0.1492 | 0.8658 | 0.9197 | 0.8919 |
| 0.1668 | 3.0 | 675 | 0.1431 | 0.8783 | 0.9277 | 0.9023 |
| 0.1668 | 4.0 | 900 | 0.1431 | 0.8793 | 0.9217 | 0.9 |
| 0.1612 | 5.0 | 1125 | 0.1403 | 0.8827 | 0.9217 | 0.9018 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1
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Base model
DeepPavlov/rubert-base-cased