Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use thedavidhackett/roberta-police-mission-statement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thedavidhackett/roberta-police-mission-statement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thedavidhackett/roberta-police-mission-statement")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("thedavidhackett/roberta-police-mission-statement") model = AutoModelForSequenceClassification.from_pretrained("thedavidhackett/roberta-police-mission-statement", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-police-mission-statement
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2607
- Accuracy: 0.9233
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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 198 | 0.2544 | 0.9006 |
| No log | 2.0 | 396 | 0.1832 | 0.9119 |
| 0.3159 | 3.0 | 594 | 0.2537 | 0.9347 |
| 0.3159 | 4.0 | 792 | 0.1902 | 0.9347 |
| 0.3159 | 5.0 | 990 | 0.2607 | 0.9233 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for thedavidhackett/roberta-police-mission-statement
Base model
FacebookAI/roberta-large