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("sakren/distil-bert")
model = AutoModelForSequenceClassification.from_pretrained("sakren/distil-bert")Quick Links
distil-bert
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: 0.1749
- F1: 0.9295
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
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.8295 | 1.0 | 250 | 0.2760 | 0.9148 |
| 0.2167 | 2.0 | 500 | 0.1838 | 0.9326 |
| 0.1461 | 3.0 | 750 | 0.1749 | 0.9295 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for sakren/distil-bert
Base model
distilbert/distilbert-base-uncasedDataset used to train sakren/distil-bert
Evaluation results
- F1 on emotionvalidation set self-reported0.930
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sakren/distil-bert")