mirror of
https://github.com/wassname/prob_jsonformer.git
synced 2026-08-20 12:40:20 +08:00
rename to be closer to json schema
This commit is contained in:
wassname
@@ -19,60 +19,46 @@ json_schema = {
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"type": "object",
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"properties": {
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# we can return the probability of each choice, even if they are multiple tokens
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"age_probs": {"type": "choice_probs", "enum": [str(s) for s in range(10, 30)]},
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"age_probs": {"type": "p_enum", "values": [str(s) for s in range(10, 20)]},
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# we can return the probabilistic weighted mean of a range
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"age_wmean": {"type": "range_mean", "minimum": 10, "maximum": 30},
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"age_wmean": {"type": "p_integer", "minimum": 10, "maximum": 20},
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# the prob of true and false
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"is_student_probs": {"type": "choice_probs", "enum": ["true", "false"]},
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"is_student_probs": {"type": "p_enum", "values": ["true", "false"]},
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"is_student": {"type": "boolean"},
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# we've merged patches for enum, integer, null, union - currently mising from jsonformer
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"name": {"type": "string", "maxLength": 4},
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"age": {"type": "integer"},
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"unit_time": {"type": "number"},
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"courses": {
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"type": "array",
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"items": {"type": "string"}
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},
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"courses": {"type": "array", "items": {"type": "string"}},
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"trim": {"type": ["string", "null"]},
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"color": {
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"type": "enum",
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"values": ["red", "green", "blue", "brown", "white", "black"],
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},
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}
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},
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}
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prompt = "Generate a young person's information based on the following schema:"
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jsonformer = Jsonformer(model, tokenizer, json_schema, prompt, temperature=0)
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generated_data = jsonformer()
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generated_data = {
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"age_probs": [
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{"prob": 0.94091796875, "choice": "10"},
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{"prob": 0.033233642578125, "choice": "20"},
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{"prob": 0.0122222900390625, "choice": "12"},
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{"prob": 0.00412750244140625, "choice": "21"},
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{"prob": 0.0028362274169921875, "choice": "16"},
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{"prob": 0.0018453598022460938, "choice": "15"},
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{"prob": 0.00113677978515625, "choice": "11"},
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{"prob": 0.0011110305786132812, "choice": "18"},
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{"prob": 0.0005083084106445312, "choice": "25"},
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{"prob": 0.0004558563232421875, "choice": "23"},
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{"prob": 0.0002498626708984375, "choice": "14"},
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{"prob": 0.00023281574249267578, "choice": "13"},
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{"prob": 0.0002238750457763672, "choice": "22"},
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{"prob": 0.00018131732940673828, "choice": "26"},
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{"prob": 0.0001690387725830078, "choice": "24"},
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{"prob": 0.00012552738189697266, "choice": "19"},
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{"prob": 7.796287536621094e-05, "choice": "27"},
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{"prob": 7.265806198120117e-05, "choice": "28"},
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{"prob": 4.106760025024414e-05, "choice": "17"},
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{"prob": 2.5033950805664062e-06, "choice": "29"},
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{"prob": 0.62353515625, "choice": "10"},
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{"prob": 0.349609375, "choice": "12"},
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{"prob": 0.01123809814453125, "choice": "11"},
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{"prob": 0.00760650634765625, "choice": "16"},
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{"prob": 0.0025482177734375, "choice": "13"},
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{"prob": 0.0025081634521484375, "choice": "15"},
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{"prob": 0.0018062591552734375, "choice": "14"},
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{"prob": 0.00104522705078125, "choice": "18"},
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{"prob": 0.00011551380157470703, "choice": "17"},
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{"prob": 5.042552947998047e-05, "choice": "19"},
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],
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"age_wmean": 17.816404402256012,
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"age_wmean": 15.544570922851562,
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"is_student_probs": [
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{"prob": 0.974609375, "choice": "true"},
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{"prob": 0.025177001953125, "choice": "false"},
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{"prob": 0.962890625, "choice": "true"},
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{"prob": 0.037322998046875, "choice": "false"},
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],
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"is_student": False,
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"name": "John",
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@@ -80,7 +66,7 @@ generated_data = {
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"unit_time": 0.5,
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"courses": ["C++"],
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"trim": None,
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"color": "white",
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"color": "green",
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}
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```
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@@ -0,0 +1,214 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# autoreload your package\n",
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loading model and tokenizer...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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" warnings.warn(\n",
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"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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" warnings.warn(\n",
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"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loaded model and tokenizer\n"
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]
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}
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],
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"source": [
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"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
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"import torch\n",
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"\n",
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"print(\"Loading model and tokenizer...\")\n",
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"model_name = \"databricks/dolly-v2-3b\"\n",
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"model = AutoModelForCausalLM.from_pretrained(\n",
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" model_name,\n",
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" use_cache=True,\n",
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" torch_dtype=torch.float16,\n",
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" attn_implementation=\"eager\",\n",
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").to(\"cuda:0\")\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_cache=True)\n",
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"print(\"Loaded model and tokenizer\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'age_probs': [{'prob': 0.94091796875, 'choice': '10'},\n",
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" {'prob': 0.033233642578125, 'choice': '20'},\n",
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" {'prob': 0.0122222900390625, 'choice': '12'},\n",
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" {'prob': 0.00412750244140625, 'choice': '21'},\n",
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" {'prob': 0.0028362274169921875, 'choice': '16'},\n",
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" {'prob': 0.0018453598022460938, 'choice': '15'},\n",
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" {'prob': 0.00113677978515625, 'choice': '11'},\n",
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" {'prob': 0.0011110305786132812, 'choice': '18'},\n",
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" {'prob': 0.0005083084106445312, 'choice': '25'},\n",
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" {'prob': 0.0004558563232421875, 'choice': '23'},\n",
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" {'prob': 0.0002498626708984375, 'choice': '14'},\n",
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" {'prob': 0.00023281574249267578, 'choice': '13'},\n",
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" {'prob': 0.0002238750457763672, 'choice': '22'},\n",
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" {'prob': 0.00018131732940673828, 'choice': '26'},\n",
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" {'prob': 0.0001690387725830078, 'choice': '24'},\n",
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" {'prob': 0.00012552738189697266, 'choice': '19'},\n",
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" {'prob': 7.796287536621094e-05, 'choice': '27'},\n",
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" {'prob': 7.265806198120117e-05, 'choice': '28'},\n",
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" {'prob': 4.106760025024414e-05, 'choice': '17'},\n",
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" {'prob': 2.5033950805664062e-06, 'choice': '29'}],\n",
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" 'age_wmean': 17.816404402256012,\n",
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" 'is_student_probs': [{'prob': 0.974609375, 'choice': 'true'},\n",
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" {'prob': 0.025177001953125, 'choice': 'false'}],\n",
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" 'is_student': False,\n",
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" 'name': 'John',\n",
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" 'age': 17,\n",
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" 'unit_time': 0.5,\n",
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" 'courses': ['C++'],\n",
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" 'trim': None,\n",
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" 'color': 'white'}"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from prob_jsonformer import Jsonformer\n",
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"\n",
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"json_schema = {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" # we can return the probability of each choice, even if they are multiple tokens\n",
|
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" \"age_probs\": {\"type\": \"p_enum\", \"enum\": [str(s) for s in range(10, 30)]},\n",
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" # we can return the probabilistic weighted mean of a range\n",
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" \"age_wmean\": {\"type\": \"p_integer\", \"minimum\": 10, \"maximum\": 30},\n",
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" # the prob of true and false\n",
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" \"is_student_probs\": {\"type\": \"p_enum\", \"enum\": [\"true\", \"false\"]},\n",
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" \"is_student\": {\"type\": \"boolean\"},\n",
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" # we've merged patches for enum, integer, null, union - currently mising from jsonformer\n",
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" \"name\": {\"type\": \"string\", \"maxLength\": 4},\n",
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" \"age\": {\"type\": \"integer\"},\n",
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" \"unit_time\": {\"type\": \"number\"},\n",
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" \"courses\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n",
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" \"trim\": {\"type\": [\"string\", \"null\"]},\n",
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" \"color\": {\n",
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" \"type\": \"enum\",\n",
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" \"values\": [\"red\", \"green\", \"blue\", \"brown\", \"white\", \"black\"],\n",
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" },\n",
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" },\n",
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"}\n",
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"\n",
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"\n",
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"prompt = \"Generate a young person's information based on the following schema:\"\n",
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"jsonformer = Jsonformer(model, tokenizer, json_schema, prompt)\n",
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"generated_data = jsonformer()\n",
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"\n",
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"generated_data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"generated_data = {\n",
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" \"age_probs\": [\n",
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" {\"prob\": 0.94091796875, \"choice\": \"10\"},\n",
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" {\"prob\": 0.033233642578125, \"choice\": \"20\"},\n",
|
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" {\"prob\": 0.0122222900390625, \"choice\": \"12\"},\n",
|
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" {\"prob\": 0.00412750244140625, \"choice\": \"21\"},\n",
|
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" {\"prob\": 0.0028362274169921875, \"choice\": \"16\"},\n",
|
||||
" {\"prob\": 0.0018453598022460938, \"choice\": \"15\"},\n",
|
||||
" {\"prob\": 0.00113677978515625, \"choice\": \"11\"},\n",
|
||||
" {\"prob\": 0.0011110305786132812, \"choice\": \"18\"},\n",
|
||||
" {\"prob\": 0.0005083084106445312, \"choice\": \"25\"},\n",
|
||||
" {\"prob\": 0.0004558563232421875, \"choice\": \"23\"},\n",
|
||||
" {\"prob\": 0.0002498626708984375, \"choice\": \"14\"},\n",
|
||||
" {\"prob\": 0.00023281574249267578, \"choice\": \"13\"},\n",
|
||||
" {\"prob\": 0.0002238750457763672, \"choice\": \"22\"},\n",
|
||||
" {\"prob\": 0.00018131732940673828, \"choice\": \"26\"},\n",
|
||||
" {\"prob\": 0.0001690387725830078, \"choice\": \"24\"},\n",
|
||||
" {\"prob\": 0.00012552738189697266, \"choice\": \"19\"},\n",
|
||||
" {\"prob\": 7.796287536621094e-05, \"choice\": \"27\"},\n",
|
||||
" {\"prob\": 7.265806198120117e-05, \"choice\": \"28\"},\n",
|
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" {\"prob\": 4.106760025024414e-05, \"choice\": \"17\"},\n",
|
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" {\"prob\": 2.5033950805664062e-06, \"choice\": \"29\"},\n",
|
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" ],\n",
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" \"age_wmean\": 17.816404402256012,\n",
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" \"is_student_probs\": [\n",
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" {\"prob\": 0.974609375, \"choice\": \"true\"},\n",
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" {\"prob\": 0.025177001953125, \"choice\": \"false\"},\n",
|
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" ],\n",
|
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" \"is_student\": False,\n",
|
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" \"name\": \"John\",\n",
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" \"age\": 17,\n",
|
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" \"unit_time\": 0.5,\n",
|
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" \"courses\": [\"C++\"],\n",
|
||||
" \"trim\": None,\n",
|
||||
" \"color\": \"white\",\n",
|
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"}"
|
||||
]
|
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}
|
||||
],
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"metadata": {
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"kernelspec": {
|
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"display_name": ".venv",
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"language": "python",
|
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"name": "python3"
|
||||
},
|
||||
"language_info": {
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"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
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"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
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+356
-449
@@ -1,452 +1,359 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# autoreload your package\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||||
" from .autonotebook import tqdm as notebook_tqdm\n"
|
||||
]
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# autoreload your package\n",
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||||
" from .autonotebook import tqdm as notebook_tqdm\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loading model and tokenizer...\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
||||
" warnings.warn(\n",
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
||||
" warnings.warn(\n",
|
||||
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loaded model and tokenizer\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
|
||||
"import torch\n",
|
||||
"\n",
|
||||
"print(\"Loading model and tokenizer...\")\n",
|
||||
"model_name = \"databricks/dolly-v2-3b\"\n",
|
||||
"model = AutoModelForCausalLM.from_pretrained(\n",
|
||||
" model_name,\n",
|
||||
" use_cache=True,\n",
|
||||
" torch_dtype=torch.float16,\n",
|
||||
" attn_implementation=\"eager\",\n",
|
||||
").to(\"cuda:0\")\n",
|
||||
"tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_cache=True)\n",
|
||||
"print(\"Loaded model and tokenizer\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Continue"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from prob_jsonformer.format import highlight_values\n",
|
||||
"from prob_jsonformer.main import Jsonformer\n",
|
||||
"\n",
|
||||
"ecomm = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"store\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"name\": {\"type\": \"string\"},\n",
|
||||
" \"location\": {\"type\": \"string\"},\n",
|
||||
" \"p_enum\": {\n",
|
||||
" \"type\": \"p_enum\",\n",
|
||||
" \"enum\": [\"ski\", \"snowboard\", \"walk\", \"pretend\"],\n",
|
||||
" },\n",
|
||||
" \"inventory\": {\n",
|
||||
" \"type\": \"array\",\n",
|
||||
" \"items\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"productId\": {\"type\": \"string\"},\n",
|
||||
" \"name\": {\"type\": \"string\"},\n",
|
||||
" \"description\": {\"type\": \"string\"},\n",
|
||||
" \"category\": {\"type\": \"string\"},\n",
|
||||
" \"price\": {\"type\": \"number\"},\n",
|
||||
" \"inStock\": {\"type\": \"boolean\"},\n",
|
||||
" \"rating\": {\"type\": \"number\"},\n",
|
||||
" \"images\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" }\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"builder = Jsonformer(\n",
|
||||
" model=model,\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" json_schema=ecomm,\n",
|
||||
" prompt=\"write a description about mike's ski shop which sells premium skis and snowboards\",\n",
|
||||
" max_string_token_length=20,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Generating...\")\n",
|
||||
"output = builder()\n",
|
||||
"\n",
|
||||
"highlight_values(output)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"car = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"make\": {\"type\": \"string\"},\n",
|
||||
" \"model\": {\"type\": \"p_enum\", \"enum\": [\"Mazda\", \"Kea\"]},\n",
|
||||
" \"new\": {\"type\": \"p_enum\", \"enum\": [\"true\", \"false\"]},\n",
|
||||
" \"rating\": {\"type\": \"p_enum\", \"enum\": [\"1\", \"2\", \"3\", \"4\"]},\n",
|
||||
" \"year\": {\"type\": \"number\"},\n",
|
||||
" \"colors_available\": {\n",
|
||||
" \"type\": \"array\",\n",
|
||||
" \"items\": {\"type\": \"string\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"builder = Jsonformer(\n",
|
||||
" model=model,\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" json_schema=car,\n",
|
||||
" prompt=\"generate an example car\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Generating...\")\n",
|
||||
"output = builder()\n",
|
||||
"\n",
|
||||
"highlight_values(output)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"complex_car = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"car\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"make\": {\"type\": \"string\"},\n",
|
||||
" \"model\": {\"type\": \"string\"},\n",
|
||||
" \"year\": {\"type\": \"number\"},\n",
|
||||
" \"colors\": {\n",
|
||||
" \"type\": \"p_enum\",\n",
|
||||
" \"enum\": [\"red\", \"green\", \"blue\", \"black\", \"white\"],\n",
|
||||
" },\n",
|
||||
" \"as_new\": {\"type\": \"p_enum\", \"enum\": [\"true\", \"false\"]},\n",
|
||||
" \"rating\": {\"type\": \"p_enum\", \"enum\": [\"1\", \"2\", \"3\", \"4\"]},\n",
|
||||
" \"features\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"audio\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"brand\": {\"type\": \"string\"},\n",
|
||||
" \"speakers\": {\"type\": \"number\"},\n",
|
||||
" \"hasBluetooth\": {\"type\": \"boolean\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"safety\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"airbags\": {\"type\": \"number\"},\n",
|
||||
" \"parkingSensors\": {\"type\": \"boolean\"},\n",
|
||||
" \"laneAssist\": {\"type\": \"boolean\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"performance\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"engine\": {\"type\": \"string\"},\n",
|
||||
" \"horsepower\": {\"type\": \"number\"},\n",
|
||||
" \"topSpeed\": {\"type\": \"number\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"owner\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"firstName\": {\"type\": \"string\"},\n",
|
||||
" \"lastName\": {\"type\": \"string\"},\n",
|
||||
" \"age\": {\"type\": \"number\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"builder = Jsonformer(\n",
|
||||
" model=model,\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" json_schema=complex_car,\n",
|
||||
" prompt=\"generate an example Rolls Royce Phantom\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Generating...\")\n",
|
||||
"output = builder()\n",
|
||||
"\n",
|
||||
"highlight_values(output)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Readme example"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
|
||||
"\n",
|
||||
"model_name = \"databricks/dolly-v2-3b\"\n",
|
||||
"model = AutoModelForCausalLM.from_pretrained(model_name)\n",
|
||||
"tokenizer = AutoTokenizer.from_pretrained(model_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'age_probs': [{'prob': 0.62353515625, 'choice': '10'},\n",
|
||||
" {'prob': 0.349609375, 'choice': '12'},\n",
|
||||
" {'prob': 0.01123809814453125, 'choice': '11'},\n",
|
||||
" {'prob': 0.00760650634765625, 'choice': '16'},\n",
|
||||
" {'prob': 0.0025482177734375, 'choice': '13'},\n",
|
||||
" {'prob': 0.0025081634521484375, 'choice': '15'},\n",
|
||||
" {'prob': 0.0018062591552734375, 'choice': '14'},\n",
|
||||
" {'prob': 0.00104522705078125, 'choice': '18'},\n",
|
||||
" {'prob': 0.00011551380157470703, 'choice': '17'},\n",
|
||||
" {'prob': 5.042552947998047e-05, 'choice': '19'}],\n",
|
||||
" 'age_wmean': 15.544570922851562,\n",
|
||||
" 'is_student_probs': [{'prob': 0.962890625, 'choice': 'true'},\n",
|
||||
" {'prob': 0.037322998046875, 'choice': 'false'}],\n",
|
||||
" 'is_student': False,\n",
|
||||
" 'name': 'John',\n",
|
||||
" 'age': 17,\n",
|
||||
" 'unit_time': 0.5,\n",
|
||||
" 'courses': ['C++'],\n",
|
||||
" 'trim': None,\n",
|
||||
" 'color': 'green'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from prob_jsonformer import Jsonformer\n",
|
||||
"\n",
|
||||
"json_schema = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" # we can return the probability of each choice, even if they are multiple tokens\n",
|
||||
" \"age_probs\": {\"type\": \"p_enum\", \"values\": [str(s) for s in range(10, 20)]},\n",
|
||||
" # we can return the probabilistic weighted mean of a range\n",
|
||||
" \"age_wmean\": {\"type\": \"p_integer\", \"minimum\": 10, \"maximum\": 20},\n",
|
||||
" # the prob of true and false\n",
|
||||
" \"is_student_probs\": {\"type\": \"p_enum\", \"values\": [\"true\", \"false\"]},\n",
|
||||
" \"is_student\": {\"type\": \"boolean\"},\n",
|
||||
" # we've merged patches for enum, integer, null, union - currently mising from jsonformer\n",
|
||||
" \"name\": {\"type\": \"string\", \"maxLength\": 4},\n",
|
||||
" \"age\": {\"type\": \"integer\"},\n",
|
||||
" \"unit_time\": {\"type\": \"number\"},\n",
|
||||
" \"courses\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n",
|
||||
" \"trim\": {\"type\": [\"string\", \"null\"]},\n",
|
||||
" \"color\": {\n",
|
||||
" \"type\": \"enum\",\n",
|
||||
" \"values\": [\"red\", \"green\", \"blue\", \"brown\", \"white\", \"black\"],\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"prompt = \"Generate a young person's information based on the following schema:\"\n",
|
||||
"jsonformer = Jsonformer(model, tokenizer, json_schema, prompt)\n",
|
||||
"generated_data = jsonformer()\n",
|
||||
"\n",
|
||||
"generated_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.16"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loading model and tokenizer...\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
||||
" warnings.warn(\n",
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
||||
" warnings.warn(\n",
|
||||
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loaded model and tokenizer\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
|
||||
"import torch\n",
|
||||
"\n",
|
||||
"print(\"Loading model and tokenizer...\")\n",
|
||||
"model_name = \"databricks/dolly-v2-3b\"\n",
|
||||
"model = AutoModelForCausalLM.from_pretrained(\n",
|
||||
" model_name,\n",
|
||||
" use_cache=True,\n",
|
||||
" torch_dtype=torch.float16,\n",
|
||||
" attn_implementation='eager',\n",
|
||||
").to(\"cuda:0\")\n",
|
||||
"tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_cache=True)\n",
|
||||
"print(\"Loaded model and tokenizer\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Continue"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Generating...\n",
|
||||
"{\n",
|
||||
" store: {\n",
|
||||
" name: \u001b[32m\"Mike's Ski Shop\"\u001b[0m,\n",
|
||||
" location: \u001b[32m\"Somewhere\"\u001b[0m,\n",
|
||||
" choice_probs: [\n",
|
||||
" {\n",
|
||||
" prob: \u001b[32m0.01739501953125\u001b[0m,\n",
|
||||
" choice: \u001b[32m\"pretend\"\u001b[0m\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" prob: \u001b[32m0.002094268798828125\u001b[0m,\n",
|
||||
" choice: \u001b[32m\"snowboard\"\u001b[0m\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" prob: \u001b[32m0.0007467269897460938\u001b[0m,\n",
|
||||
" choice: \u001b[32m\"walk\"\u001b[0m\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" prob: \u001b[32m0.97998046875\u001b[0m,\n",
|
||||
" choice: \u001b[32m\"ski\"\u001b[0m\n",
|
||||
" }\n",
|
||||
" ],\n",
|
||||
" inventory: [\n",
|
||||
" {\n",
|
||||
" productId: \u001b[32m\"1\"\u001b[0m,\n",
|
||||
" name: \u001b[32m\"Snowboard X-15\"\u001b[0m,\n",
|
||||
" description: \u001b[32m\"Snowboard for all levels\"\u001b[0m,\n",
|
||||
" category: \u001b[32m\"Snowboards\"\u001b[0m,\n",
|
||||
" price: \u001b[32m20.0\u001b[0m,\n",
|
||||
" inStock: \u001b[32mTrue\u001b[0m,\n",
|
||||
" rating: \u001b[32m5.0\u001b[0m,\n",
|
||||
" images: [\n",
|
||||
" \u001b[32m\"https://s3.amazonaws.com/mikesskisport/images/Snow\"\u001b[0m\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" productId: \u001b[32m\"2\"\u001b[0m,\n",
|
||||
" name: \u001b[32m\"Mike's Ski Shop Exclusive\"\u001b[0m,\n",
|
||||
" description: \u001b[32m\"Mike's Ski Shop Exclusive\"\u001b[0m,\n",
|
||||
" category: \u001b[32m\"Ski Shops\"\u001b[0m,\n",
|
||||
" price: \u001b[32m20.0\u001b[0m,\n",
|
||||
" inStock: \u001b[32mTrue\u001b[0m,\n",
|
||||
" rating: \u001b[32m5.0\u001b[0m,\n",
|
||||
" images: [\n",
|
||||
" \u001b[32m\"https://s3.amazonaws.com/mikesskisport/images/Mike\"\u001b[0m\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" productId: \u001b[32m\"3\"\u001b[0m,\n",
|
||||
" name: \u001b[32m\"Mike's Ski Shop Exclusive\"\u001b[0m,\n",
|
||||
" description: \u001b[32m\"Mike's Ski Shop Exclusive\"\u001b[0m,\n",
|
||||
" category: \u001b[32m\"Ski Shops\"\u001b[0m,\n",
|
||||
" price: \u001b[32m20.0\u001b[0m,\n",
|
||||
" inStock: \u001b[32mTrue\u001b[0m,\n",
|
||||
" rating: \u001b[32m5.0\u001b[0m,\n",
|
||||
" images: [\n",
|
||||
" \u001b[32m\"https://s3.amazonaws.com/mikesskisport/images/Mike\"\u001b[0m\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
"}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from prob_jsonformer.format import highlight_values\n",
|
||||
"from prob_jsonformer.main import Jsonformer\n",
|
||||
"\n",
|
||||
"ecomm = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"store\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"name\": {\"type\": \"string\"},\n",
|
||||
" \"location\": {\"type\": \"string\"},\n",
|
||||
" \"choice_probs\": {\"type\": \"choice_probs\", \"enum\": [\"ski\", \"snowboard\", \"walk\", \"pretend\"]},\n",
|
||||
" \"inventory\": {\n",
|
||||
" \"type\": \"array\",\n",
|
||||
" \"items\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"productId\": {\"type\": \"string\"},\n",
|
||||
" \"name\": {\"type\": \"string\"},\n",
|
||||
" \"description\": {\"type\": \"string\"},\n",
|
||||
" \"category\": {\"type\": \"string\"},\n",
|
||||
" \"price\": {\"type\": \"number\"},\n",
|
||||
" \"inStock\": {\"type\": \"boolean\"},\n",
|
||||
" \"rating\": {\"type\": \"number\"},\n",
|
||||
" \"images\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" }\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"builder = Jsonformer(\n",
|
||||
" model=model,\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" json_schema=ecomm,\n",
|
||||
" prompt=\"write a description about mike's ski shop which sells premium skis and snowboards\",\n",
|
||||
" max_string_token_length=20,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Generating...\")\n",
|
||||
"output = builder()\n",
|
||||
"\n",
|
||||
"highlight_values(output)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"car = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"make\": {\"type\": \"string\"},\n",
|
||||
" \"model\": {\"type\": \"choice_probs\", \"enum\": [\"Mazda\", \"Kea\"]},\n",
|
||||
" \"new\": {\"type\": \"choice_probs\", \"enum\": [\"true\", \"false\"]},\n",
|
||||
" \"rating\": {\"type\": \"choice_probs\", \"enum\": [\"1\", \"2\", \"3\", \"4\"]},\n",
|
||||
" \"year\": {\"type\": \"number\"},\n",
|
||||
" \"colors_available\": {\n",
|
||||
" \"type\": \"array\",\n",
|
||||
" \"items\": {\"type\": \"string\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"builder = Jsonformer(\n",
|
||||
" model=model,\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" json_schema=car,\n",
|
||||
" prompt=\"generate an example car\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Generating...\")\n",
|
||||
"output = builder()\n",
|
||||
"\n",
|
||||
"highlight_values(output)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"complex_car = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"car\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"make\": {\"type\": \"string\"},\n",
|
||||
" \"model\": {\"type\": \"string\"},\n",
|
||||
" \"year\": {\"type\": \"number\"},\n",
|
||||
" \"colors\": {\"type\": \"choice_probs\", \"enum\": [\"red\", \"green\", \"blue\", \"black\", \"white\"]},\n",
|
||||
" \"as_new\": {\"type\": \"choice_probs\", \"enum\": [\"true\", \"false\"]},\n",
|
||||
" \"rating\": {\"type\": \"choice_probs\", \"enum\": [\"1\", \"2\", \"3\", \"4\"]},\n",
|
||||
" \"features\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"audio\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"brand\": {\"type\": \"string\"},\n",
|
||||
" \"speakers\": {\"type\": \"number\"},\n",
|
||||
" \"hasBluetooth\": {\"type\": \"boolean\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"safety\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"airbags\": {\"type\": \"number\"},\n",
|
||||
" \"parkingSensors\": {\"type\": \"boolean\"},\n",
|
||||
" \"laneAssist\": {\"type\": \"boolean\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"performance\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"engine\": {\"type\": \"string\"},\n",
|
||||
" \"horsepower\": {\"type\": \"number\"},\n",
|
||||
" \"topSpeed\": {\"type\": \"number\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" \"owner\": {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"firstName\": {\"type\": \"string\"},\n",
|
||||
" \"lastName\": {\"type\": \"string\"},\n",
|
||||
" \"age\": {\"type\": \"number\"},\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
"}\n",
|
||||
"builder = Jsonformer(\n",
|
||||
" model=model,\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" json_schema=complex_car,\n",
|
||||
" prompt=\"generate an example Rolls Royce Phantom\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Generating...\")\n",
|
||||
"output = builder()\n",
|
||||
"\n",
|
||||
"highlight_values(output)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Readme example"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from prob_jsonformer import Jsonformer\n",
|
||||
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
|
||||
"\n",
|
||||
"model_name = \"databricks/dolly-v2-3b\"\n",
|
||||
"model = AutoModelForCausalLM.from_pretrained(model_name)\n",
|
||||
"tokenizer = AutoTokenizer.from_pretrained(model_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||||
" from .autonotebook import tqdm as notebook_tqdm\n",
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
||||
" warnings.warn(\n",
|
||||
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
|
||||
"/media/wassname/SGIronWolf/projects5/2024/prob_jsonformer/.venv/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:492: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'name': 'John',\n",
|
||||
" 'age': 20,\n",
|
||||
" 'age_probs': [{'prob': 0.856144905090332, 'choice': '12'},\n",
|
||||
" {'prob': 0.045701637864112854, 'choice': '10'},\n",
|
||||
" {'prob': 0.030096691101789474, 'choice': '20'},\n",
|
||||
" {'prob': 0.01899518258869648, 'choice': '11'},\n",
|
||||
" {'prob': 0.013291668146848679, 'choice': '16'},\n",
|
||||
" {'prob': 0.013288195244967937, 'choice': '14'},\n",
|
||||
" {'prob': 0.011642636731266975, 'choice': '18'},\n",
|
||||
" {'prob': 0.005356263369321823, 'choice': '15'},\n",
|
||||
" {'prob': 0.0035301733296364546, 'choice': '13'},\n",
|
||||
" {'prob': 0.0010820770403370261, 'choice': '21'},\n",
|
||||
" {'prob': 0.0003798121470026672, 'choice': '19'},\n",
|
||||
" {'prob': 0.0002950581256300211, 'choice': '17'},\n",
|
||||
" {'prob': 7.64212163630873e-05, 'choice': '22'},\n",
|
||||
" {'prob': 4.703202284872532e-05, 'choice': '23'},\n",
|
||||
" {'prob': 2.3594444428454153e-05, 'choice': '25'},\n",
|
||||
" {'prob': 1.987080577237066e-05, 'choice': '24'},\n",
|
||||
" {'prob': 1.821534169721417e-05, 'choice': '26'},\n",
|
||||
" {'prob': 9.411132850800641e-06, 'choice': '28'},\n",
|
||||
" {'prob': 7.120665941329207e-07, 'choice': '27'},\n",
|
||||
" {'prob': 4.4407053678696684e-07, 'choice': '29'}],\n",
|
||||
" 'age_wmean': 18.284568134928122,\n",
|
||||
" 'unit_time': 0.5,\n",
|
||||
" 'is_student': True,\n",
|
||||
" 'is_student_probs': [{'prob': 0.885669469833374, 'choice': 'true'},\n",
|
||||
" {'prob': 0.1143304631114006, 'choice': 'false'}],\n",
|
||||
" 'courses': ['C1'],\n",
|
||||
" 'trim': None,\n",
|
||||
" 'color': 'white'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"json_schema = {\n",
|
||||
" \"type\": \"object\",\n",
|
||||
" \"properties\": {\n",
|
||||
" \"name\": {\"type\": \"string\", \"maxLength\": 4},\n",
|
||||
" \"age_probs\": {\"type\": \"choice_probs\", \"enum\": [str(s) for s in range(10, 30)]},\n",
|
||||
" \"age_wmean\": {\"type\": \"range_mean\", \"minimum\": 10, \"maximum\": 30},\n",
|
||||
" \"is_student_probs\": {\"type\": \"choice_probs\", \"enum\": [\"true\", \"false\"]},\n",
|
||||
" \"is_student\": {\"type\": \"boolean\"},\n",
|
||||
" \"age\": {\"type\": \"integer\"},\n",
|
||||
" \"unit_time\": {\"type\": \"number\"},\n",
|
||||
" \"courses\": {\n",
|
||||
" \"type\": \"array\",\n",
|
||||
" \"items\": {\"type\": \"string\"}\n",
|
||||
" },\n",
|
||||
" \"trim\": {\"type\": [\"string\", \"null\"]},\n",
|
||||
" \"color\": {\n",
|
||||
" \"type\": \"enum\",\n",
|
||||
" \"values\": [\"red\", \"green\", \"blue\", \"brown\", \"white\", \"black\"],\n",
|
||||
" },\n",
|
||||
" }\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"prompt = \"Generate a young person's information based on the following schema:\"\n",
|
||||
"jsonformer = Jsonformer(model, tokenizer, json_schema, prompt, temperature=0)\n",
|
||||
"generated_data = jsonformer()\n",
|
||||
"\n",
|
||||
"generated_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.16"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
||||
@@ -181,12 +181,12 @@ class Jsonformer:
|
||||
|
||||
return response.split('"')[0].strip()
|
||||
|
||||
def generate_choice_probs(self, choices) -> str:
|
||||
def generate_p_enum(self, choices) -> str:
|
||||
"""
|
||||
This is not in the json schema, but can be usefull for effeciently getting the prob distibution over choices
|
||||
"""
|
||||
prompt = self.get_prompt() + '"'
|
||||
self.debug("[generate_string_prob]", prompt, is_prompt=True)
|
||||
self.debug("[generate_p_enum]", prompt, is_prompt=True)
|
||||
input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(
|
||||
self.model.device
|
||||
)[0]
|
||||
@@ -194,14 +194,14 @@ class Jsonformer:
|
||||
choices_tokens = [torch.tensor(c) for c in choices_tokens]
|
||||
|
||||
r = list(choice_tree(self.model, self.tokenizer, input_ids, choices_tokens))
|
||||
return r # json.dumps(r)
|
||||
return r
|
||||
|
||||
def generate_range_mean(self, range_min: float, range_max: float) -> float:
|
||||
def generate_p_integer(self, range_min: float, range_max: float) -> float:
|
||||
"""
|
||||
This is not in the json schema, but can be usefull for effeciently generating the weighted mean from a range of integers
|
||||
"""
|
||||
choices = [str(n) for n in range(int(range_min), int(range_max) + 1)]
|
||||
result = self.generate_choice_probs(choices)
|
||||
result = self.generate_p_enum(choices)
|
||||
|
||||
# now do a weighted average
|
||||
total = 0.0
|
||||
@@ -332,18 +332,18 @@ class Jsonformer:
|
||||
return self.generate_string(
|
||||
schema["maxLength"] if "maxLength" in schema else None
|
||||
)
|
||||
elif schema_type == "choice_probs":
|
||||
elif schema_type == "p_enum":
|
||||
if key:
|
||||
obj[key] = self.generation_marker
|
||||
else:
|
||||
obj.append(self.generation_marker)
|
||||
return self.generate_choice_probs(schema["enum"])
|
||||
elif schema_type == "range_mean":
|
||||
return self.generate_p_enum(schema["values"])
|
||||
elif schema_type == "p_integer":
|
||||
if key:
|
||||
obj[key] = self.generation_marker
|
||||
else:
|
||||
obj.append(self.generation_marker)
|
||||
return self.generate_range_mean(schema["minimum"], schema["maximum"])
|
||||
return self.generate_p_integer(schema["minimum"], schema["maximum"])
|
||||
elif schema_type == "enum":
|
||||
if key:
|
||||
obj[key] = self.generation_marker
|
||||
|
||||
Reference in New Issue
Block a user