README.md

---
license:
- apache-2.0
- cc-by-nc-4.0
datasets: pszemraj/fleece2instructions-codealpaca
tags:
- generated_from_trainer
- instruct
- instructions
- code
- instructiongen
metrics:
- rouge
language:
- en
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 {
      pageContent: string;

      // eslint-disable-next-line @typescript-eslint/no-explicit-any
      metadata: Record<string, any>;
    }

    /**
     * Interface for interacting with a document.
     */
    export class Document implements DocumentParams {
      pageContent: string;

      // eslint-disable-next-line @typescript-eslint/no-explicit-any
      metadata: Record<string, any>;

      constructor(fields?: Partial<DocumentParams>) {
        this.pageContent = fields?.pageContent ?? this.pageContent;
        this.metadata = fields?.metadata ?? {};
      }
    }
  example_title: js
- text: |
    def merge(left, right):
        if len(left) == 0:
            return right

        if len(right) == 0:
            return left

        result = []
        index_left = index_right = 0

        while len(result) < len(left) + len(right):
            if left[index_left] <= right[index_right]:
                result.append(left[index_left])
                index_left += 1
            else:
                result.append(right[index_right])
                index_right += 1

            if index_right == len(right):
                result += left[index_left:]
                break

            if index_left == len(left):
                result += right[index_right:]
                break

        return result
  example_title: merge
- text: >
    import pandas as pd

    import plotly.graph_objects as go


    df =
    pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv')


    fig = go.Figure(go.Scatter(x = df['AAPL_x'], y = df['AAPL_y'],
                      name='Share Prices (in USD)'))

    fig.update_layout(title='Apple Share Prices over time (2014)',
                       plot_bgcolor='rgb(230, 230,230)',
                       showlegend=True)

    fig.show()
  example_title: plot
- text: |
    from spellchecker import SpellChecker

    spell = SpellChecker()

    def check_word_spelling(word: str):
        misspelled = spell.unknown([word])
        return len(misspelled) == 0

    def eval_and_replace(text: str, match_token: str = "- "):
        if match_token not in text:
            return text
        else:
            while True:
                full_before_text = text.split(match_token, maxsplit=1)[0]
                before_text = [
                    char for char in full_before_text.split()[-1] if char.isalpha()
                ]
                before_text = "".join(before_text)
                full_after_text = text.split(match_token, maxsplit=1)[-1]
                after_text = [char for char in full_after_text.split()[0] if char.isalpha()]
                after_text = "".join(after_text)
                full_text = before_text + after_text
                if check_word_spelling(full_text):
                    text = full_before_text + full_after_text
                else:
                    text = full_before_text + " " + full_after_text
                if match_token not in text:
                    break
            return text

    text = "I- am- a go- od- boy"
    eval_and_replace(text)
  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
---


# bart-base-code-instructiongen

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

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co./facebook/bart-base) on the `pszemraj/fleece2instructions-codealpaca` dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0136
- Rouge1: 59.9513
- Rouge2: 33.9118
- Rougel: 55.7815
- Rougelsum: 56.9064
- Gen Len: 29.7146

## 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**. 

> This is just a `base` size model, which does a decent job for its size, but is not perfect. For better quality instructions, check out [bart-large](https://huggingface.co./pszemraj/bart-large-code-instructiongen) or fine tune your own larger model on the dataset :)

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: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.02
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.1165        | 1.0   | 281  | 1.1090          | 57.9239 | 31.9259 | 53.8737 | 54.9811   | 28.2924 |
| 1.0763        | 2.0   | 563  | 1.0267          | 59.9605 | 34.0298 | 55.7523 | 56.8021   | 29.6966 |
| 0.9595        | 2.99  | 843  | 1.0136          | 59.9513 | 33.9118 | 55.7815 | 56.9064   | 29.7146 |