Transformers
PyTorch
TensorBoard
English
encoder-decoder
text2text-generation
Generated from Trainer
seq2seq
Instructions to use abhi11nav/codebert-gpt2-commitgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhi11nav/codebert-gpt2-commitgen with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("abhi11nav/codebert-gpt2-commitgen") model = AutoModelForSeq2SeqLM.from_pretrained("abhi11nav/codebert-gpt2-commitgen") - Notebooks
- Google Colab
- Kaggle
CodeTrans OutPerforms Using T5
#1
by kevinlu1248 - opened
Just had some brief experimentation and so far it looks like CodeTrans, even using T5-small outperforms this model. Simple example being the following
diff --git a/docs/index.md b/docs/index.md
index c1750d5..7859157 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -78,7 +78,7 @@ id: home
<hr class="home-divider" />
<section class="home-bottom-section">
<div class="buttons-unit">
- <a href="/getting-started.html" class="button">Get Started</a>
- <a href="/download.html" class="button">Download React v{{site.react_version}}</a>
+ <a href="docs/getting-started.html" class="button">Get Started</a>
+ <a href="downloads.html" class="button">Download React v{{site.react_version}}</a>
</div>
</section>
CodeTrans yields "Fix broken link to version" while GPT2-commitgen yields "Fix typo in README". I used https://huggingface.co/SEBIS/code_trans_t5_small_commit_generation_transfer_learning_finetune.
I think this is likely due to CodeTrans being a fine-tuned model, having seen a lot of code. I'm still a bit surprised as GPT2-commitgen is larger and uses a better decoder.
Nvm, just realized GPT-2 is older than T5.