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---
license: mit
tags:
- generated_from_trainer
base_model: openai-community/gpt2
model-index:
- name: gpt2-finetuned-mcqa-sciq
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-mcqa-sciq
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3533
- Bertscore Precision: 0.1082
- Bertscore Recall: 0.1141
- Bertscore F1: 0.1111
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bertscore Precision | Bertscore Recall | Bertscore F1 |
|:-------------:|:------:|:-----:|:---------------:|:-------------------:|:----------------:|:------------:|
| 4.4695 | 0.9999 | 5839 | 2.3612 | 0.1082 | 0.1140 | 0.1110 |
| 4.0507 | 2.0 | 11679 | 2.3533 | 0.1082 | 0.1141 | 0.1111 |
| 3.8779 | 2.9999 | 17518 | 2.3820 | 0.1080 | 0.1140 | 0.1110 |
| 3.2852 | 4.0 | 23358 | 2.4208 | 0.1080 | 0.1140 | 0.1109 |
| 3.6416 | 4.9999 | 29197 | 2.4768 | 0.1079 | 0.1139 | 0.1108 |
| 2.9843 | 6.0 | 35037 | 2.5445 | 0.1079 | 0.1139 | 0.1108 |
| 2.8509 | 6.9999 | 40876 | 2.6094 | 0.1079 | 0.1139 | 0.1108 |
| 2.6932 | 8.0 | 46716 | 2.6658 | 0.1078 | 0.1138 | 0.1107 |
| 2.5309 | 8.9999 | 52555 | 2.7283 | 0.1078 | 0.1138 | 0.1107 |
| 2.5619 | 9.9991 | 58390 | 2.7585 | 0.1078 | 0.1138 | 0.1107 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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