Instructions to use DanielNRU/pollen-ner2-800 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DanielNRU/pollen-ner2-800 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-ner2-800") - Notebooks
- Google Colab
- Kaggle
pollen-ner2-800
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: 0.2265
- Precision: 0.7546
- Recall: 0.8273
- F1: 0.7893
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 | 100 | 0.2447 | 0.7059 | 0.8193 | 0.7584 |
| No log | 2.0 | 200 | 0.2398 | 0.7180 | 0.8233 | 0.7671 |
| No log | 3.0 | 300 | 0.2361 | 0.7326 | 0.8253 | 0.7762 |
| No log | 4.0 | 400 | 0.2313 | 0.7406 | 0.8313 | 0.7833 |
| 0.5116 | 5.0 | 500 | 0.2265 | 0.7546 | 0.8273 | 0.7893 |
| 0.5116 | 6.0 | 600 | 0.2334 | 0.7220 | 0.8293 | 0.7720 |
| 0.5116 | 7.0 | 700 | 0.2255 | 0.7446 | 0.8313 | 0.7856 |
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/bert-base-bg-cs-pl-ru-cased