# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("lenatr99/fine_tuned_boolq_bert")
model = AutoModelForSequenceClassification.from_pretrained("lenatr99/fine_tuned_boolq_bert")Quick Links
fine_tuned_boolq_bert
This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5736
- Accuracy: 0.7222
- F1: 0.7325
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6443 | 4.1667 | 50 | 0.5606 | 0.7778 | 0.6806 |
| 0.3932 | 8.3333 | 100 | 0.6016 | 0.6111 | 0.6255 |
| 0.126 | 12.5 | 150 | 1.0887 | 0.5 | 0.5418 |
| 0.0166 | 16.6667 | 200 | 1.5543 | 0.5556 | 0.5829 |
| 0.0041 | 20.8333 | 250 | 1.5032 | 0.7222 | 0.7325 |
| 0.0022 | 25.0 | 300 | 1.7354 | 0.6667 | 0.6872 |
| 0.0018 | 29.1667 | 350 | 1.5756 | 0.6667 | 0.6667 |
| 0.0016 | 33.3333 | 400 | 1.5736 | 0.7222 | 0.7325 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for lenatr99/fine_tuned_boolq_bert
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lenatr99/fine_tuned_boolq_bert")