# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ValeraDanger/electra_test")
model = AutoModelForSequenceClassification.from_pretrained("ValeraDanger/electra_test")Quick Links
electra_test
This model is a fine-tuned version of ai-forever/ruElectra-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0062
- F1: 0.7532
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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.5713 | 1.0 | 515 | 0.5318 | 0.7528 |
| 0.4262 | 2.0 | 1030 | 0.6691 | 0.7530 |
| 0.256 | 3.0 | 1545 | 0.7394 | 0.7635 |
| 0.1025 | 4.0 | 2060 | 1.0062 | 0.7532 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
- Downloads last month
- 2
Model tree for ValeraDanger/electra_test
Base model
ai-forever/ruElectra-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ValeraDanger/electra_test")