Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use dimsafin/arxiv_distilbert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dimsafin/arxiv_distilbert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dimsafin/arxiv_distilbert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dimsafin/arxiv_distilbert_model") model = AutoModelForSequenceClassification.from_pretrained("dimsafin/arxiv_distilbert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
arxiv_distilbert_model
This model is a fine-tuned version of dimsafin/arxiv_distilbert_model on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2123
- Accuracy: 0.9483
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: 768
- eval_batch_size: 192
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3048 | 1.0 | 42 | 0.2128 | 0.949 |
| 0.2781 | 2.0 | 84 | 0.2163 | 0.9486 |
| 0.2533 | 3.0 | 126 | 0.2168 | 0.9474 |
| 0.2394 | 4.0 | 168 | 0.2157 | 0.9484 |
| 0.2295 | 5.0 | 210 | 0.2178 | 0.948 |
| 0.2083 | 6.0 | 252 | 0.2137 | 0.9474 |
| 0.2099 | 7.0 | 294 | 0.2134 | 0.948 |
| 0.1857 | 8.0 | 336 | 0.2127 | 0.9481 |
| 0.1657 | 9.0 | 378 | 0.2130 | 0.9482 |
| 0.1712 | 10.0 | 420 | 0.2123 | 0.9483 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.4.0+cu121
- Datasets 3.3.0
- Tokenizers 0.21.1
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