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---
language:
- en
tags:
- text-generation
- gpt2
- gpt
license: mit
widget:
- text: |+
Do you like my new haircut?
person beta:
example_title: haircut
- text: |+
I love to learn new things.. are you willing to teach me something?
person beta:
example_title: teaching
- text: |+
What's your favorite animal? Mine is the dog?
person beta:
example_title: favorite
- text: |+
how much does it cost?
person beta:
example_title: money
inference:
parameters:
min_length: 2
max_length: 64
length_penalty: 0.6
no_repeat_ngram_size: 3
do_sample: true
top_p: 0.85
top_k: 10
repetition_penalty: 2.1
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. -->
# pszemraj/gpt2-medium-vaguely-human-dialogue
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on a parsed version of Wizard of Wikipedia. Because the batch size was so large, it learned a general understanding of words that makes sense together but does not specifically respond to anything - sort of like an alien learning to imitate human words to convince others that it is human.
It achieves the following results on the evaluation set:
- Loss: 4.3281
## Model description
- a decent example of what happens when your batch size is too large and the global optima does not reflect specific prompts / use cases.
## Intended uses & limitations
- there are no intended uses
## Training and evaluation data
- a parsed version of the wizard of Wikipedia dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 34.991 | 1.0 | 837 | 14.8359 |
| 12.2881 | 2.0 | 1674 | 9.375 |
| 8.5071 | 3.0 | 2511 | 7.2148 |
| 7.6031 | 4.0 | 3348 | 6.1758 |
| 6.4808 | 5.0 | 4185 | 5.5820 |
| 5.8562 | 6.0 | 5022 | 5.0977 |
| 5.6094 | 7.0 | 5859 | 4.8203 |
| 5.2591 | 8.0 | 6696 | 4.5977 |
| 5.0031 | 9.0 | 7533 | 4.4219 |
| 4.8837 | 10.0 | 8370 | 4.3281 |
### Framework versions
- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.0 |