Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use w6666/models-moved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w6666/models-moved with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="w6666/models-moved")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("w6666/models-moved") model = AutoModelForSequenceClassification.from_pretrained("w6666/models-moved", device_map="auto") - Notebooks
- Google Colab
- Kaggle
models
This model was trained from scratch on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 0.2801
- Accuracy: 0.936
- F1: 0.9359
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.4034 | 1.0 | 1000 | 0.2211 | 0.921 | 0.9218 |
| 0.1681 | 2.0 | 2000 | 0.1970 | 0.93 | 0.9288 |
| 0.1171 | 3.0 | 3000 | 0.1928 | 0.9375 | 0.9373 |
| 0.0807 | 4.0 | 4000 | 0.2077 | 0.936 | 0.9363 |
| 0.0446 | 5.0 | 5000 | 0.2801 | 0.936 | 0.9359 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
- Downloads last month
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Evaluation results
- Accuracy on arrowvalidation set self-reported0.936
- F1 on arrowvalidation set self-reported0.936