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
metadata
library_name: transformers
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: pollen-re-model
results: []
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: 0.4025
- 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