---
language:
- en
license:
- apache-2.0
- cc-by-nc-4.0
tags:
- generated_from_trainer
- instruct
- instructions
- code
- instructiongen
datasets: pszemraj/fleece2instructions-codealpaca
metrics:
- rouge
widget:
- text: 'git lfs install

    huggingface-cli lfs-enable-largefiles .

    git lfs track "*.bin"

    git add .

    git commit -a -m "add fp32 chkpt"

    git push

    '
  example_title: bash
- text: "export interface DocumentParams {\n  pageContent: string;\n\n  // eslint-disable-next-line\
    \ @typescript-eslint/no-explicit-any\n  metadata: Record<string, any>;\n}\n\n\
    /**\n * Interface for interacting with a document.\n */\nexport class Document\
    \ implements DocumentParams {\n  pageContent: string;\n\n  // eslint-disable-next-line\
    \ @typescript-eslint/no-explicit-any\n  metadata: Record<string, any>;\n\n  constructor(fields?:\
    \ Partial<DocumentParams>) {\n    this.pageContent = fields?.pageContent ?? this.pageContent;\n\
    \    this.metadata = fields?.metadata ?? {};\n  }\n}\n"
  example_title: js
- text: "def merge(left, right):\n    if len(left) == 0:\n        return right\n\n\
    \    if len(right) == 0:\n        return left\n\n    result = []\n    index_left\
    \ = index_right = 0\n\n    while len(result) < len(left) + len(right):\n     \
    \   if left[index_left] <= right[index_right]:\n            result.append(left[index_left])\n\
    \            index_left += 1\n        else:\n            result.append(right[index_right])\n\
    \            index_right += 1\n\n        if index_right == len(right):\n     \
    \       result += left[index_left:]\n            break\n\n        if index_left\
    \ == len(left):\n            result += right[index_right:]\n            break\n\
    \n    return result\n"
  example_title: merge
- text: "import pandas as pd\nimport plotly.graph_objects as go\n\ndf = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')\n\
    \nfig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],\n           \
    \       name='Share Prices (in USD)'))\n\nfig.update_layout(title='Apple Share\
    \ Prices over time (2014)',\n                   plot_bgcolor='rgb(230, 230,230)',\n\
    \                   showlegend=True)\n\nfig.show()\n"
  example_title: plot
- text: "from spellchecker import SpellChecker\n\nspell = SpellChecker()\n\ndef check_word_spelling(word:\
    \ str):\n    misspelled = spell.unknown([word])\n    return len(misspelled) ==\
    \ 0\n\ndef eval_and_replace(text: str, match_token: str = \"- \"):\n    if match_token\
    \ not in text:\n        return text\n    else:\n        while True:\n        \
    \    full_before_text = text.split(match_token, maxsplit=1)[0]\n            before_text\
    \ = [\n                char for char in full_before_text.split()[-1] if char.isalpha()\n\
    \            ]\n            before_text = \"\".join(before_text)\n           \
    \ full_after_text = text.split(match_token, maxsplit=1)[-1]\n            after_text\
    \ = [char for char in full_after_text.split()[0] if char.isalpha()]\n        \
    \    after_text = \"\".join(after_text)\n            full_text = before_text +\
    \ after_text\n            if check_word_spelling(full_text):\n               \
    \ text = full_before_text + full_after_text\n            else:\n             \
    \   text = full_before_text + \" \" + full_after_text\n            if match_token\
    \ not in text:\n                break\n        return text\n\ntext = \"I- am-\
    \ a go- od- boy\"\neval_and_replace(text)\n"
  example_title: spell check
- text: 'import torch

    from transformers import AutoTokenizer, AutoModelForSequenceClassification


    checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"

    tokenizer = AutoTokenizer.from_pretrained(checkpoint)

    model = AutoModelForSequenceClassification.from_pretrained(checkpoint)

    sequences = ["I''ve been waiting for a HuggingFace course my whole life.", "So
    have I!"]


    tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")

    output = model(**tokens)

    '
  example_title: model inference
inference:
  parameters:
    max_length: 96
    num_beams: 4
base_model: facebook/bart-large
---


# bart-large-code-instructiongen

Use this text2text model to find out what LLM instructions might be able to generate an arbitary piece of code!

- Check out a [basic demo on Spaces](https://huggingface.co./spaces/pszemraj/generate-instructions)
- An example of how to use instructiongen models in a CLI script can be found [here](https://gist.github.com/pszemraj/8b0213e700763106074d3ac15d041c14)
- You can find other models fine-tuned for instruction generation by [searching for the instructiongen tag](https://huggingface.co./models?other=instructiongen)

## about 

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co./facebook/bart-large) on the `pszemraj/fleece2instructions-codealpaca` dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9222
- Rouge1: 62.0692
- Rouge2: 36.1947
- Rougel: 57.5128
- Rougelsum: 58.6613
- Gen Len: 31.0060


## Intended uses & limitations

🚨 **note:** as the authors elected to release the [original dataset](https://github.com/sahil280114/codealpaca) under `cc-by-nc`, the license carries over to this model and **cannot be used for commercial activity**. 

Intended use: Research on domain adaptation and/or other improvements to LLMs by extending instruction:text data pairs.

## Training and evaluation data

Refer to the linked dataset card for `pszemraj/fleece2instructions-codealpaca` or the [original dataset](https://github.com/sahil280114/codealpaca) repo.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0914        | 1.0   | 563  | 1.0303          | 60.288  | 34.1884 | 55.9293 | 57.0714   | 30.6267 |
| 0.8688        | 2.0   | 1126 | 0.9333          | 61.0409 | 34.9823 | 56.4887 | 57.6662   | 31.7255 |
| 0.6773        | 3.0   | 1689 | 0.9222          | 62.0692 | 36.1947 | 57.5128 | 58.6613   | 31.0060 |