Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,75 @@
|
|
| 1 |
---
|
|
|
|
|
|
|
| 2 |
license: apache-2.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
+
language:
|
| 3 |
+
- code
|
| 4 |
license: apache-2.0
|
| 5 |
+
tags:
|
| 6 |
+
- code
|
| 7 |
+
- gpt2
|
| 8 |
+
- generation
|
| 9 |
+
datasets:
|
| 10 |
+
- "codeparrot/github-code-clean"
|
| 11 |
+
- "openai_humaneval"
|
| 12 |
+
metrics:
|
| 13 |
+
- "evaluate-metric/code_eval"
|
| 14 |
---
|
| 15 |
+
|
| 16 |
+
# CodeParrot-Multi 🦜 (small)
|
| 17 |
+
|
| 18 |
+
CodeParrot-Multi 🦜 is a GPT-2 model (110M parameters) trained to generate code in 32 programming languages (Python, Java, C, JavaScript...)
|
| 19 |
+
|
| 20 |
+
## Usage
|
| 21 |
+
|
| 22 |
+
You can load the CodeParrot-Multi model and tokenizer directly in `transformers`:
|
| 23 |
+
|
| 24 |
+
```Python
|
| 25 |
+
from transformers import AutoTokenizer, AutoModelWithLMHead
|
| 26 |
+
|
| 27 |
+
tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-multi")
|
| 28 |
+
model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small-multi")
|
| 29 |
+
|
| 30 |
+
inputs = tokenizer("def hello_world():", return_tensors="pt")
|
| 31 |
+
outputs = model(**inputs)
|
| 32 |
+
```
|
| 33 |
+
|
| 34 |
+
or with a `pipeline`:
|
| 35 |
+
|
| 36 |
+
```Python
|
| 37 |
+
from transformers import pipeline
|
| 38 |
+
|
| 39 |
+
pipe = pipeline("text-generation", model="codeparrot/codeparrot-small-multi")
|
| 40 |
+
outputs = pipe("def hello_world():")
|
| 41 |
+
```
|
| 42 |
+
|
| 43 |
+
## Training
|
| 44 |
+
|
| 45 |
+
The model was trained on the cleaned [Github code dataset](https://huggingface.co/datasets/codeparrot/github-code-clean) with the following settings:
|
| 46 |
+
|
| 47 |
+
|Config|Value|
|
| 48 |
+
|-------|-----|
|
| 49 |
+
|Batch size| 192 |
|
| 50 |
+
|Context size| 1024 |
|
| 51 |
+
|Training steps| 300'000|
|
| 52 |
+
|Gradient accumulation| 2|
|
| 53 |
+
|Gradient checkpointing| False|
|
| 54 |
+
|Learning rate| 5e-4 |
|
| 55 |
+
|Weight decay | 0.1 |
|
| 56 |
+
|Warmup steps| 2000 |
|
| 57 |
+
|Schedule| Cosine |
|
| 58 |
+
|
| 59 |
+
The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 58 billion tokens.
|
| 60 |
+
|
| 61 |
+
## Performance
|
| 62 |
+
|
| 63 |
+
We evaluated the model on OpenAI's [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark which consists of programming challenges:
|
| 64 |
+
|
| 65 |
+
| Metric | Value |
|
| 66 |
+
|-------|-----|
|
| 67 |
+
|pass@1 | --% |
|
| 68 |
+
|pass@10 | --% |
|
| 69 |
+
|pass@100 | --% |
|
| 70 |
+
|
| 71 |
+
The [pass@k metric](https://huggingface.co/metrics/code_eval) tells the probability that at least one out of k generations passes the tests.
|
| 72 |
+
|
| 73 |
+
## Resources
|
| 74 |
+
|
| 75 |
+
- Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot)
|