Text Classification
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distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("snowian/emotion")
model = AutoModelForSequenceClassification.from_pretrained("snowian/emotion")Quick Links
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: 0.1702
- Accuracy: 0.9285
- F1: 0.9287
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: 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.8067 | 1.0 | 250 | 0.2883 | 0.9115 | 0.9115 |
| 0.2204 | 2.0 | 500 | 0.1883 | 0.9295 | 0.9299 |
| 0.1495 | 3.0 | 750 | 0.1702 | 0.9285 | 0.9287 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for snowian/emotion
Base model
distilbert/distilbert-base-uncasedDataset used to train snowian/emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported0.928
- F1 on emotionvalidation set self-reported0.929
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="snowian/emotion")