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("edbeeching/test-trainer-to-hub")
model = AutoModelForSequenceClassification.from_pretrained("edbeeching/test-trainer-to-hub")Quick Links
test-trainer-to-hub
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: 0.7352
- Accuracy: 0.8456
- F1: 0.8938
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 459 | 0.4489 | 0.8235 | 0.8792 |
| 0.5651 | 2.0 | 918 | 0.4885 | 0.8260 | 0.8811 |
| 0.3525 | 3.0 | 1377 | 0.7352 | 0.8456 | 0.8938 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
- Downloads last month
- 6
Dataset used to train edbeeching/test-trainer-to-hub
Evaluation results
- Accuracy on glueself-reported0.846
- F1 on glueself-reported0.894
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edbeeching/test-trainer-to-hub")