Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Seher99/Emotion")
model = AutoModelForSequenceClassification.from_pretrained("Seher99/Emotion")Quick Links
distilbert-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1375
- Accuracy: 0.939
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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 0.1948 | 0.924 |
| 0.3384 | 2.0 | 500 | 0.1375 | 0.939 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
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Dataset used to train Seher99/Emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported0.939
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Seher99/Emotion")