Instructions to use djsull/begging_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djsull/begging_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="djsull/begging_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("djsull/begging_classification") model = AutoModelForSequenceClassification.from_pretrained("djsull/begging_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: begging_classification | |
| 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. --> | |
| # begging_classification | |
| - Loss: 0.0023 | |
| - Precision: 1.0 | |
| - Recall: 1.0 | |
| - F1: 1.0 | |
| - Accuracy: 1.0 | |
| ## 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: 5e-05 | |
| - train_batch_size: 64 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | No log | 1.0 | 9 | 0.0986 | 1.0 | 0.92 | 0.9583 | 0.96 | | |
| | No log | 2.0 | 18 | 0.0059 | 1.0 | 1.0 | 1.0 | 1.0 | | |
| | No log | 3.0 | 27 | 0.0032 | 1.0 | 1.0 | 1.0 | 1.0 | | |
| | No log | 4.0 | 36 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 | | |
| | No log | 5.0 | 45 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | | |
| ### Framework versions | |
| - Transformers 4.42.3 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.19.1 |