metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
- code
metrics:
- accuracy
model-index:
- name: bert-base-uncased
results: []
datasets:
- NTU-NLP-sg/xCodeEval
language:
- en
pipeline_tag: text-classification
bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the xCodeEval dataset, more precisely on the multi-tag classification task.
It achieves the following results on the evaluation set:
- Loss: 0.2880
- F1 Macro: 0.3305
- F1 Micro: 0.6076
- Roc Auc: 0.8857
- Accuracy: 0.4314
- Hamming Loss: 0.1064
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Roc Auc | Accuracy | Hamming Loss |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 287 | 0.3286 | 0.1706 | 0.4810 | 0.8514 | 0.3137 | 0.1270 |
| 0.3527 | 2.0 | 574 | 0.2958 | 0.3283 | 0.6029 | 0.8760 | 0.4196 | 0.1059 |
| 0.3527 | 3.0 | 861 | 0.2880 | 0.3305 | 0.6076 | 0.8857 | 0.4314 | 0.1064 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.2.2
- Datasets 3.0.0
- Tokenizers 0.19.1