Instructions to use lizaboiarchuk/rubert-tiny2-war-posts-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lizaboiarchuk/rubert-tiny2-war-posts-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lizaboiarchuk/rubert-tiny2-war-posts-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lizaboiarchuk/rubert-tiny2-war-posts-finetuned") model = AutoModelForMaskedLM.from_pretrained("lizaboiarchuk/rubert-tiny2-war-posts-finetuned") - Notebooks
- Google Colab
- Kaggle
rubert-tiny2-war-posts-finetuned
This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.7097
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.3638 | 1.0 | 1011 | 3.9762 |
| 4.1361 | 2.0 | 2022 | 3.8631 |
| 4.036 | 3.0 | 3033 | 3.7991 |
| 3.9467 | 4.0 | 4044 | 3.7706 |
| 3.8936 | 5.0 | 5055 | 3.7258 |
| 3.8732 | 6.0 | 6066 | 3.7155 |
| 3.8431 | 7.0 | 7077 | 3.6837 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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