metadata
library_name: transformers
license: mit
base_model: openai-gpt
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: gpt1_sst2_right
results: []
datasets:
- nyu-mll/glue
- stanfordnlp/sst2
gpt1_sst2_right
This model is a fine-tuned version of openai-gpt on sst2 dataset of GLUE benchmark. It achieves the following results on the evaluation set:
- Loss: 0.4216
- Accuracy: 0.9255
- Recall: 0.9369
- Precision: 0.9183
For testing, the model is loaded as a pipeline, and used for the prediction of each sample in test split. The samples and their predictions are recorded in test_preds.csv file. Access to Repository for finetuning.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
For batched training, <pad> token is added to the tokenizer and the following padding-truncation options are adapted:
- Padding Side: "right"
- Truncation Side: "right"
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision |
|---|---|---|---|---|---|---|
| 0.2 | 1.0 | 4210 | 0.2958 | 0.9037 | 0.8649 | 0.9412 |
| 0.1455 | 2.0 | 8420 | 0.3172 | 0.9186 | 0.9505 | 0.8960 |
| 0.0892 | 3.0 | 12630 | 0.3637 | 0.9278 | 0.9257 | 0.9320 |
| 0.0584 | 4.0 | 16840 | 0.4216 | 0.9255 | 0.9369 | 0.9183 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0