Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DRAGOO/bert_model")
model = AutoModelForSequenceClassification.from_pretrained("DRAGOO/bert_model")Quick Links
model_results
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: 0.1240
- Accuracy: 0.9780
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|
| 0.53 | 1.0 | 1270 | 0.3255 | 0.9425 |
| 0.2706 | 2.0 | 2540 | 0.2034 | 0.9630 |
| 0.1923 | 3.0 | 3810 | 0.1934 | 0.9685 |
| 0.1241 | 4.0 | 5080 | 0.1370 | 0.9783 |
| 0.0978 | 5.0 | 6350 | 0.1240 | 0.9780 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for DRAGOO/bert_model
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DRAGOO/bert_model")