# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mudogruer/electra-emotion")
model = AutoModelForSequenceClassification.from_pretrained("mudogruer/electra-emotion")Quick Links
electra-emotion
This model is a fine-tuned version of google/electra-base-discriminator on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1403
- Accuracy: 0.944
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6777 | 1.0 | 500 | 0.2635 | 0.9155 |
| 0.186 | 2.0 | 1000 | 0.1598 | 0.935 |
| 0.113 | 3.0 | 1500 | 0.1403 | 0.944 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for mudogruer/electra-emotion
Base model
google/electra-base-discriminatorDataset used to train mudogruer/electra-emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported0.944
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mudogruer/electra-emotion")