Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("thanhcong2001/Multiple_Labels")
model = AutoModelForSequenceClassification.from_pretrained("thanhcong2001/Multiple_Labels")Quick Links
Multiple_Labels
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8555
- Acc: 0.6609
- F1: 0.6545
- Recall: 0.6609
- Precision: 0.6557
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 0.5
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.8388 | 0.5 | 2801 | 0.8555 | 0.6609 | 0.6545 | 0.6609 | 0.6557 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
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Model tree for thanhcong2001/Multiple_Labels
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thanhcong2001/Multiple_Labels")