Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sgugger/finetuned-bert")
model = AutoModelForSequenceClassification.from_pretrained("sgugger/finetuned-bert")Quick Links
finetuned-bert
This model is a fine-tuned version of bert-base-cased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.3916
- Accuracy: 0.875
- F1: 0.9125
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: 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.581 | 1.0 | 230 | 0.4086 | 0.8260 | 0.8711 |
| 0.366 | 2.0 | 460 | 0.3758 | 0.8480 | 0.8963 |
| 0.2328 | 3.0 | 690 | 0.3916 | 0.875 | 0.9125 |
Framework versions
- Transformers 4.9.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 1.8.1.dev0
- Tokenizers 0.10.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/finetuned-bert")