---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_gpt2_clm-model
  results: []
datasets:
- eli5
language:
- en
metrics:
- perplexity
pipeline_tag: text-generation
---

<!-- 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. -->

# fine_tuned_gpt2_clm-model

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3066


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 142  | 3.3422          |
| No log        | 2.0   | 284  | 3.3226          |
| No log        | 3.0   | 426  | 3.3148          |
| 3.4352        | 4.0   | 568  | 3.3095          |
| 3.4352        | 5.0   | 710  | 3.3074          |
| 3.4352        | 6.0   | 852  | 3.3066          |
| 3.4352        | 7.0   | 994  | 3.3046          |
| 3.3068        | 8.0   | 1136 | 3.3049          |
| 3.3068        | 9.0   | 1278 | 3.3048          |
| 3.3068        | 10.0  | 1420 | 3.3050          |
| 3.2433        | 11.0  | 1562 | 3.3062          |
| 3.2433        | 12.0  | 1704 | 3.3059          |
| 3.2433        | 13.0  | 1846 | 3.3062          |
| 3.2433        | 14.0  | 1988 | 3.3065          |
| 3.2113        | 15.0  | 2130 | 3.3066          |

### Inference:

- prompt = "dna phosphorylation is the process of"

- generated Text: dna phosphorylation is the process of forming the deoxygenated product. For example, in a protein phosphorylation inhibitor, it occurs to deoxygenate the phosphorylated protein by binding a phosphate molecule and preventing it from being destroyed by a nonenzymatic process.

In a phosphorylation inhibitor like dna, the product is phosphorylated by the phosphocreatine, a phosphorylated phosphocreatine molecule that can bind to other phosphocreatine molecules that bind to phosphocreatine. This interaction helps to separate the phosphocreatine molecule that is phosphorylated from the phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-glucose molecule that is phosphocreatine-phosphocreatine-glucose-phosphocreatine-phosphocreatine-glucose.  

In anoxidase inhibitors like dna, they are a bit more specific, more specific, and have a more complicated interaction with the phosphocreatine molecule that can bind to phosphocreatine molecules.  

I would argue that both dna-and phosphocreatine-phosphocreatine-glucose will not be able to bind to phosphocreatine because the phosphocreatine-phosphocreatine-phosphocreatine-glucose-phosphocreatine molecule that was phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-glucose, is phosphocreatine.   

That is, dna-and phosphocreatine-glucose will be able to bind to phosphocreatine because the phosphocreatine molecule that was phosphocreatine-glucose will not be phosphocreatine because the phosphocreatine-phosphocreatine-glucose molecule that was phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-glucose, is phosphocreatine.  

Edit: Added: The final point is that it can't bind phosphocreatine because that phosphocreatine molecule (a phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine molecule) can not be phosphoc

### Evaluation metric:

Perplexity: 27.29

### GPU:

 - CUDA Version: 12.1 
 - 4x Tesla T4 

### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1