Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Cheng98/bert-large-boolq")
model = AutoModelForSequenceClassification.from_pretrained("Cheng98/bert-large-boolq")Quick Links
bert-large-boolq
This model is a fine-tuned version of bert-large-cased on the GLUE BOOLQ dataset. It achieves the following results on the evaluation set:
- Loss: 1.4813
- Accuracy: 0.7190
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3
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Model tree for Cheng98/bert-large-boolq
Base model
google-bert/bert-large-casedDataset used to train Cheng98/bert-large-boolq
Evaluation results
- Accuracy on GLUE BOOLQself-reported0.719
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/bert-large-boolq")