Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-finetuned-emotion")
model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-finetuned-emotion", device_map="auto")Quick Links
distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1995
- Accuracy: 0.9365
- F1: 0.9371
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.475 | 1.0 | 503 | 0.2171 | 0.928 | 0.9292 |
| 0.1235 | 2.0 | 1006 | 0.1764 | 0.9365 | 0.9372 |
| 0.0802 | 3.0 | 1509 | 0.1788 | 0.938 | 0.9388 |
| 0.0531 | 4.0 | 2012 | 0.2005 | 0.938 | 0.9388 |
| 0.0367 | 5.0 | 2515 | 0.1995 | 0.9365 | 0.9371 |
Framework versions
- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
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Dataset used to train bhadresh-savani/distilbert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionself-reported0.936
- F1 on emotionself-reported0.937
- Accuracy on emotiontest set self-reported0.923
- Precision Macro on emotiontest set self-reported0.868
- Precision Micro on emotiontest set self-reported0.923
- Precision Weighted on emotiontest set self-reported0.927
- Recall Macro on emotiontest set self-reported0.895
- Recall Micro on emotiontest set self-reported0.923
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-finetuned-emotion")