

# 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 |
{"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\nhuggingface-cli lfs-enable-largefiles .\ngit lfs track \"*.bin\"\ngit add .\ngit commit -a -m \"add fp32 chkpt\"\ngit push\n", "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\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification\n\ncheckpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\nmodel = AutoModelForSequenceClassification.from_pretrained(checkpoint)\nsequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n\ntokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\noutput = model(**tokens)\n", "example_title": "model inference"}], "inference": {"parameters": {"max_length": 96, "num_beams": 4}}, "base_model": "facebook/bart-base"}
transformers pytorch safetensors bart text2text-generation generated_from_trainer instruct instructions code instructiongen en dataset:pszemraj/fleece2instructions-codealpaca base_model:facebook/bart-base license:apache-2.0 license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #generated_from_trainer #instruct #instructions #code #instructiongen #en #dataset-pszemraj/fleece2instructions-codealpaca #base_model-facebook/bart-base #license-apache-2.0 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 

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 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 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 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 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 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
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #generated_from_trainer #instruct #instructions #code #instructiongen #en #dataset-pszemraj/fleece2instructions-codealpaca #base_model-facebook/bart-base #license-apache-2.0 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #generated_from_trainer #instruct #instructions #code #instructiongen #en #dataset-pszemraj/fleece2instructions-codealpaca #base_model-facebook/bart-base #license-apache-2.0 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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