# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Daniel246/test_electra_small")
model = AutoModelForSequenceClassification.from_pretrained("Daniel246/test_electra_small")Quick Links
test_electra_small
This model is a fine-tuned version of google/electra-small-discriminator on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6214
- Accuracy: 0.69
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6806 | 1.0 | 13 | 0.6456 | 0.69 |
| 0.6503 | 2.0 | 26 | 0.6261 | 0.69 |
| 0.634 | 3.0 | 39 | 0.6214 | 0.69 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cpu
- Datasets 2.17.0
- Tokenizers 0.15.2
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Model tree for Daniel246/test_electra_small
Base model
google/electra-small-discriminator
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Daniel246/test_electra_small")