Instructions to use DanielNRU/pollen-re-model_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DanielNRU/pollen-re-model_ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanielNRU/pollen-re-model_")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DanielNRU/pollen-re-model_") model = AutoModelForSequenceClassification.from_pretrained("DanielNRU/pollen-re-model_", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: DeepPavlov/rubert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - f1 | |
| model-index: | |
| - name: pollen-re-model | |
| 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-re-model | |
| 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: 0.4025 | |
| - F1: 0.9359 | |
| ## 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 | 340 | 0.6404 | 0.5449 | | |
| | 0.5338 | 2.0 | 680 | 0.5035 | 0.8096 | | |
| | 0.3952 | 3.0 | 1020 | 0.4355 | 0.8571 | | |
| | 0.3952 | 4.0 | 1360 | 0.4025 | 0.9359 | | |
| | 0.2357 | 5.0 | 1700 | 0.4735 | 0.9296 | | |
| ### Framework versions | |
| - Transformers 4.51.3 | |
| - Pytorch 2.7.0+cu128 | |
| - Datasets 3.5.0 | |
| - Tokenizers 0.21.1 | |