Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sgugger/finetuned-bert-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("sgugger/finetuned-bert-mrpc", device_map="auto")Quick Links
finetuned-bert-mrpc
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: 0.4917
- Accuracy: 0.8235
- F1: 0.8792
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 |
|---|---|---|---|---|---|
| 0.5382 | 1.0 | 230 | 0.4008 | 0.8456 | 0.8893 |
| 0.3208 | 2.0 | 460 | 0.4182 | 0.8309 | 0.8844 |
| 0.1587 | 3.0 | 690 | 0.4917 | 0.8235 | 0.8792 |
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-mrpc")