Instructions to use DanielNRU/pollen-ner-650 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DanielNRU/pollen-ner-650 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-650") - Notebooks
- Google Colab
- Kaggle
pollen-ner-650
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: 0.2307
- Precision: 0.7422
- Recall: 0.8614
- F1: 0.7974
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 | 82 | 0.2548 | 0.6949 | 0.8554 | 0.7669 |
| No log | 2.0 | 164 | 0.2488 | 0.7114 | 0.8514 | 0.7751 |
| No log | 3.0 | 246 | 0.2307 | 0.7422 | 0.8614 | 0.7974 |
| No log | 4.0 | 328 | 0.2423 | 0.7145 | 0.8594 | 0.7803 |
| No log | 5.0 | 410 | 0.2414 | 0.7241 | 0.8695 | 0.7901 |
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