claudios
/

cotext-1-cc

@@ -1,199 +1,70 @@
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 ---
+language: code
+datasets:
+- code_search_net
+---
+This is an *unofficial* reupload of [razent/cotext-1-cc](https://huggingface.co/razent/cotext-1-cc) in the `SafeTensors` format using `transformers` `4.40.1`. The goal of this reupload is to prevent older models that are still relevant baselines from becoming stale as a result of changes in HuggingFace. Additionally, I may include minor corrections, such as model max length configuration.
+Original model card below:
+---
+# CoText (1-CC)
+## Introduction
+Paper: [CoTexT: Multi-task Learning with Code-Text Transformer](https://arxiv.org/abs/2105.08645)
+Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
+## How to use
+Supported languages:
+```shell
+"go"
+"java"
+"javascript"
+"php"
+"python"
+"ruby"
+```
+For more details, do check out [our Github repo](https://github.com/justinphan3110/CoTexT).
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("razent/cotext-1-cc")
+model = AutoModelForSeq2SeqLM.from_pretrained("razent/cotext-1-cc")
+sentence = "def add(a, b): return a + b"
+text =  "python: " + sentence + " </s>"
+encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
+input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
+outputs = model.generate(
+    input_ids=input_ids, attention_mask=attention_masks,
+    max_length=256,
+    early_stopping=True
+)
+for output in outputs:
+    line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    print(line)
+```
+## Citation
+```
+@inproceedings{phan-etal-2021-cotext,
+    title = "{C}o{T}ex{T}: Multi-task Learning with Code-Text Transformer",
+    author = "Phan, Long and Tran, Hieu and Le, Daniel and Nguyen, Hieu and Annibal, James and Peltekian, Alec and Ye, Yanfang",
+    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
+    year = "2021",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.nlp4prog-1.5",
+    doi = "10.18653/v1/2021.nlp4prog-1.5",
+    pages = "40--47"
+}
+```