# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("namesarnav/causalbench_code-bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("namesarnav/causalbench_code-bert-base-uncased", device_map="auto")Quick Links
causalbench_code-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6049
- Accuracy: 0.7004
- Macro F1: 0.6970
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 393 | 0.5853 | 0.6947 | 0.6936 |
| 0.6154 | 2.0 | 786 | 0.5961 | 0.6794 | 0.6671 |
| 0.5358 | 3.0 | 1179 | 0.6043 | 0.6997 | 0.6964 |
| 0.4379 | 4.0 | 1572 | 0.6865 | 0.6889 | 0.6757 |
| 0.4379 | 5.0 | 1965 | 0.7535 | 0.6762 | 0.6710 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.14.0
- Datasets 3.6.0
- Tokenizers 0.23.2
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Model tree for namesarnav/causalbench_code-bert-base-uncased
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namesarnav/causalbench_code-bert-base-uncased")