Instructions to use DanielNRU/pollen-re with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DanielNRU/pollen-re with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanielNRU/pollen-re")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DanielNRU/pollen-re") model = AutoModelForSequenceClassification.from_pretrained("DanielNRU/pollen-re", device_map="auto") - Notebooks
- Google Colab
- Kaggle
pollen-re-model
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: 0.5091
- F1: 0.9291
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 422 | 0.4688 | 0.8965 |
| 0.572 | 2.0 | 844 | 0.5048 | 0.8790 |
| 0.348 | 3.0 | 1266 | 0.4542 | 0.9217 |
| 0.2617 | 4.0 | 1688 | 0.5091 | 0.9291 |
| 0.1491 | 5.0 | 2110 | 0.5265 | 0.9291 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for DanielNRU/pollen-re
Base model
DeepPavlov/rubert-base-cased