Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use PuxAI/PII-Binary-Filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PuxAI/PII-Binary-Filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PuxAI/PII-Binary-Filter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PuxAI/PII-Binary-Filter") model = AutoModelForSequenceClassification.from_pretrained("PuxAI/PII-Binary-Filter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-multilingual-cased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: PII-Binary-Filter | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # PII-Binary-Filter | |
| This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1123 | |
| - Accuracy: 0.9583 | |
| - F1: 0.9763 | |
| - Precision: 0.9677 | |
| - Recall: 0.9851 | |
| ## 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: 64 | |
| - 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 | F1 | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | 0.1789 | 1.0 | 2015 | 0.1576 | 0.9384 | 0.9651 | 0.9571 | 0.9732 | | |
| | 0.1207 | 2.0 | 4030 | 0.1198 | 0.9559 | 0.9750 | 0.9652 | 0.9850 | | |
| | 0.0992 | 3.0 | 6045 | 0.1123 | 0.9583 | 0.9763 | 0.9677 | 0.9851 | | |
| ### Framework versions | |
| - Transformers 4.56.0 | |
| - Pytorch 2.8.0+cu129 | |
| - Datasets 4.8.2 | |
| - Tokenizers 0.22.0 | |