Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("marlex7/tiny-bert-sst2-distilled")
model = AutoModelForSequenceClassification.from_pretrained("marlex7/tiny-bert-sst2-distilled", device_map="auto")Quick Links
tiny-bert-sst2-distilled
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: 1.6692
- Accuracy: 0.7947
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: 4.787209189533254e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.4226 | 1.0 | 527 | 1.7918 | 0.7844 |
| 1.5656 | 2.0 | 1054 | 1.6692 | 0.7947 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
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Model tree for marlex7/tiny-bert-sst2-distilled
Base model
google/bert_uncased_L-2_H-128_A-2Dataset used to train marlex7/tiny-bert-sst2-distilled
Evaluation results
- Accuracy on gluevalidation set self-reported0.795
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marlex7/tiny-bert-sst2-distilled")