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("gokuls/BERT-tiny-sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/BERT-tiny-sst2")Quick Links
BERT-tiny-sst2
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4422
- Accuracy: 0.8372
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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3914 | 1.0 | 4210 | 0.4383 | 0.8211 |
| 0.2577 | 2.0 | 8420 | 0.4422 | 0.8372 |
| 0.212 | 3.0 | 12630 | 0.5460 | 0.8085 |
| 0.1862 | 4.0 | 16840 | 0.5885 | 0.8245 |
| 0.1671 | 5.0 | 21050 | 0.7159 | 0.8096 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
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Dataset used to train gokuls/BERT-tiny-sst2
Evaluation results
- Accuracy on glueself-reported0.837
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gokuls/BERT-tiny-sst2")