Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AwwadR/model")
model = AutoModelForSequenceClassification.from_pretrained("AwwadR/model")Quick Links
model
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: 0.8202
- Accuracy: 0.6334
- Macro F1: 0.6289
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: 5e-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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|---|---|---|---|---|---|
| 0.8412 | 1.0 | 748 | 0.8300 | 0.6027 | 0.6070 |
| 0.6247 | 2.0 | 1496 | 0.8202 | 0.6334 | 0.6289 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cpu
- Datasets 2.21.0
- Tokenizers 0.22.2
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Model tree for AwwadR/model
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AwwadR/model")