Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use TuringGame/distilbert-base-uncased-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TuringGame/distilbert-base-uncased-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TuringGame/distilbert-base-uncased-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TuringGame/distilbert-base-uncased-classifier") model = AutoModelForSequenceClassification.from_pretrained("TuringGame/distilbert-base-uncased-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from TuringGame/distilbert-base-uncased-classifier: direct link, hf CLI and curl.
- Browser
- Download file 2.24 kB
-
https://huggingface.co/TuringGame/distilbert-base-uncased-classifier/resolve/main/README.md
- Command line
-
hf download hf://TuringGame/distilbert-base-uncased-classifier/README.md
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curl -L -o README.md https://huggingface.co/TuringGame/distilbert-base-uncased-classifier/resolve/main/README.md
2.24 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-classifier
results: []
distilbert-base-uncased-classifier
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2505
- Accuracy: 0.8919
- F1: 0.7899
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: 32
- eval_batch_size: 32
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 0 | 0 | 0.6737 | 0.7493 | 0.0440 |
| No log | 0.2020 | 79 | 0.3465 | 0.8602 | 0.6820 |
| No log | 0.4041 | 158 | 0.3404 | 0.8458 | 0.7371 |
| No log | 0.6061 | 237 | 0.2951 | 0.8761 | 0.7650 |
| No log | 0.8082 | 316 | 0.2763 | 0.8862 | 0.7893 |
| No log | 1.0102 | 395 | 0.2732 | 0.8818 | 0.7747 |
| No log | 1.2123 | 474 | 0.2707 | 0.8905 | 0.7865 |
| 0.3426 | 1.4143 | 553 | 0.2516 | 0.8963 | 0.7989 |
| 0.3426 | 1.6164 | 632 | 0.2434 | 0.8963 | 0.7943 |
| 0.3426 | 1.8184 | 711 | 0.2505 | 0.8919 | 0.7899 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1