Instructions to use DanielNRU/pollen_ner2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DanielNRU/pollen_ner2 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_ner2") - Notebooks
- Google Colab
- Kaggle
File size: 2,319 Bytes
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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: 0.2712
- Precision: 0.7229
- Recall: 0.8434
- F1: 0.7785
## 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 | 0.7862 | 0.4691 | 0.0763 | 0.1313 |
| 1.1314 | 2.0 | 500 | 0.5175 | 0.4883 | 0.6305 | 0.5504 |
| 1.1314 | 3.0 | 750 | 0.4163 | 0.5199 | 0.7088 | 0.5998 |
| 0.6496 | 4.0 | 1000 | 0.3507 | 0.5949 | 0.7550 | 0.6655 |
| 0.6496 | 5.0 | 1250 | 0.3229 | 0.6238 | 0.7791 | 0.6929 |
| 0.51 | 6.0 | 1500 | 0.2990 | 0.6857 | 0.8193 | 0.7466 |
| 0.51 | 7.0 | 1750 | 0.2847 | 0.7075 | 0.8353 | 0.7661 |
| 0.4533 | 8.0 | 2000 | 0.2749 | 0.7133 | 0.8394 | 0.7712 |
| 0.4533 | 9.0 | 2250 | 0.2723 | 0.7216 | 0.8434 | 0.7778 |
| 0.4361 | 10.0 | 2500 | 0.2712 | 0.7229 | 0.8434 | 0.7785 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1 |