# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AIExplorer1234/results")
model = AutoModelForSequenceClassification.from_pretrained("AIExplorer1234/results", device_map="auto")Quick Links
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: 0.2719
- Accuracy: 0.9166
- Precision: 0.8914
- Recall: 0.9496
- F1: 0.9195
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: 16
- eval_batch_size: 16
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2355 | 1.0 | 2232 | 0.2344 | 0.9072 | 0.9319 | 0.8795 | 0.9049 |
| 0.1550 | 2.0 | 4464 | 0.2906 | 0.9100 | 0.8825 | 0.9468 | 0.9135 |
| 0.0939 | 3.0 | 6696 | 0.3709 | 0.9115 | 0.9031 | 0.9227 | 0.9128 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.14.0.dev20260725+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for AIExplorer1234/results
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIExplorer1234/results")