Instructions to use DanielNRU/pollen-ner2-1900 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DanielNRU/pollen-ner2-1900 with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("DeepPavlov/bert-base-bg-cs-pl-ru-cased") model = PeftModel.from_pretrained(base_model, "DanielNRU/pollen-ner2-1900") - Notebooks
- Google Colab
- Kaggle
pollen-ner2-1900
This model is a fine-tuned version of DeepPavlov/bert-base-bg-cs-pl-ru-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1668
- Precision: 0.8434
- Recall: 0.8976
- F1: 0.8696
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 | 238 | 0.1668 | 0.8434 | 0.8976 | 0.8696 |
| No log | 2.0 | 476 | 0.1640 | 0.8330 | 0.8916 | 0.8613 |
| 0.2462 | 3.0 | 714 | 0.1634 | 0.8409 | 0.8916 | 0.8655 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1
- Downloads last month
- 1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for DanielNRU/pollen-ner2-1900
Base model
DeepPavlov/bert-base-bg-cs-pl-ru-cased