Instructions to use st125338/t2e-classifier-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use st125338/t2e-classifier-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="st125338/t2e-classifier-v5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("st125338/t2e-classifier-v5") model = AutoModelForSequenceClassification.from_pretrained("st125338/t2e-classifier-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t2e-classifier-v5
This model is a fine-tuned version of st125338/t2e-classifier-v4 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6272
- Accuracy: 0.8460
- Micro F1: 0.8460
- Macro F1: 0.7382
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro F1 | Macro F1 |
|---|---|---|---|---|---|---|
| 0.1958 | 1.0 | 5449 | 0.5376 | 0.8550 | 0.8550 | 0.7363 |
| 0.1533 | 2.0 | 10898 | 0.6241 | 0.8394 | 0.8394 | 0.7368 |
| 0.1638 | 2.9995 | 16344 | 0.6272 | 0.8460 | 0.8460 | 0.7382 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.2.0+cu118
- Datasets 3.5.0
- Tokenizers 0.21.0
- Downloads last month
- 3
Model tree for st125338/t2e-classifier-v5
Base model
google-bert/bert-base-uncased Finetuned
st125338/t2e-classifier-v4