Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("P3ps/test-trainer-glue-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("P3ps/test-trainer-glue-mrpc")Quick Links
test-trainer-glue-mrpc
This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6850
- Accuracy: {'accuracy': 0.8627450980392157}
- F1: 0.9024
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 459 | 0.3762 | {'accuracy': 0.8455882352941176} | 0.8873 |
| 0.4903 | 2.0 | 918 | 0.5500 | {'accuracy': 0.8431372549019608} | 0.8923 |
| 0.2654 | 3.0 | 1377 | 0.6850 | {'accuracy': 0.8627450980392157} | 0.9024 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
- 2
Dataset used to train P3ps/test-trainer-glue-mrpc
Evaluation results
- Accuracy on gluevalidation set self-reported[object Object]
- F1 on gluevalidation set self-reported0.902
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="P3ps/test-trainer-glue-mrpc")