mirror of
https://github.com/wassname/prob_jsonformer.git
synced 2026-09-10 12:37:35 +08:00
tidy
This commit is contained in:
wassname
@@ -1,8 +1,8 @@
|
||||
# prob_jsonformer: A Bulletproof Way to Generate Probabilistic Structured JSON from Language Models.
|
||||
|
||||
This fork has been modified to include the token probabilities. The original [README](https://github.com/1rgs/jsonformer) is includesbelow.
|
||||
This fork has been modified to include the token probabilities. This is not complaint with json schema, but it can be useful for efficient extracting of a range of possible values.
|
||||
|
||||
I've also merged some of hte recent pr's for enum, integer, null, union. You can see them all below in this example:
|
||||
I've also merged some of the recent PR's for enum, integer, null, union. They are not yet included in the upstream Jsonformer. You can see them all below in this example:
|
||||
|
||||
|
||||
## Example
|
||||
@@ -18,12 +18,17 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
json_schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string"},
|
||||
"age": {"type": "integer"},
|
||||
"age_probs": {"type": "choice_probs", "enum": [str(s) for s in range(10, 20)]},
|
||||
"unit_time": {"type": "number"},
|
||||
"is_student": {"type": "boolean"},
|
||||
# we can return the probability of each choice, even if they are multiple tokens
|
||||
"age_probs": {"type": "choice_probs", "enum": [str(s) for s in range(10, 30)]},
|
||||
# we can return the probabilistic weighted mean of a range
|
||||
"age_wmean": {"type": "range_mean", "minimum": 10, "maximum": 30},
|
||||
# the prob of true and false
|
||||
"is_student_probs": {"type": "choice_probs", "enum": ["true", "false"]},
|
||||
"is_student": {"type": "boolean"},
|
||||
# we've merged patches for enum, integer, null, union - currently mising from jsonformer
|
||||
"name": {"type": "string", "maxLength": 4},
|
||||
"age": {"type": "integer"},
|
||||
"unit_time": {"type": "number"},
|
||||
"courses": {
|
||||
"type": "array",
|
||||
"items": {"type": "string"}
|
||||
@@ -36,31 +41,45 @@ json_schema = {
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
prompt = "Generate a young person's information based on the following schema:"
|
||||
jsonformer = Jsonformer(model, tokenizer, json_schema, prompt, temperature=0)
|
||||
generated_data = jsonformer()
|
||||
|
||||
generated_data = {'name': 'John Doe',
|
||||
'age': 20,
|
||||
'age_probs': [{'prob': 0.794921875, 'choice': '12'},
|
||||
{'prob': 0.068359375, 'choice': '10'},
|
||||
{'prob': 0.04345703125, 'choice': '16'},
|
||||
{'prob': 0.03228759765625, 'choice': '14'},
|
||||
{'prob': 0.0175628662109375, 'choice': '11'},
|
||||
{'prob': 0.0157318115234375, 'choice': '15'},
|
||||
{'prob': 0.006664276123046875, 'choice': '18'},
|
||||
{'prob': 0.0046539306640625, 'choice': '13'},
|
||||
{'prob': 0.00041294097900390625, 'choice': '17'},
|
||||
{'prob': 0.00028824806213378906, 'choice': '19'},,
|
||||
'unit_time': 0.01,
|
||||
'is_student': True,
|
||||
'is_student_probs': [{'prob': 0.8310546875, 'choice': 'true'},
|
||||
{'prob': 0.1688232421875, 'choice': 'false'}],
|
||||
'courses': ['C1'],
|
||||
generated_data = {'name': 'John',
|
||||
'age_probs': [{'prob': 0.62353515625, 'choice': '10'},
|
||||
{'prob': 0.276611328125, 'choice': '12'},
|
||||
{'prob': 0.05364990234375, 'choice': '20'},
|
||||
{'prob': 0.0257415771484375, 'choice': '11'},
|
||||
{'prob': 0.0047607421875, 'choice': '15'},
|
||||
{'prob': 0.004688262939453125, 'choice': '16'},
|
||||
{'prob': 0.002910614013671875, 'choice': '18'},
|
||||
{'prob': 0.0024127960205078125, 'choice': '13'},
|
||||
{'prob': 0.0015821456909179688, 'choice': '14'},
|
||||
{'prob': 0.0013532638549804688, 'choice': '23'},
|
||||
{'prob': 0.0012521743774414062, 'choice': '21'},
|
||||
{'prob': 0.00042247772216796875, 'choice': '17'},
|
||||
{'prob': 0.0003342628479003906, 'choice': '22'},
|
||||
{'prob': 0.0002484321594238281, 'choice': '19'},
|
||||
{'prob': 0.0001995563507080078, 'choice': '25'},
|
||||
{'prob': 4.851818084716797e-05, 'choice': '24'},
|
||||
{'prob': 3.30805778503418e-05, 'choice': '26'},
|
||||
{'prob': 2.6404857635498047e-05, 'choice': '28'},
|
||||
{'prob': 1.728534698486328e-05, 'choice': '27'},
|
||||
{'prob': 2.9802322387695312e-06, 'choice': '29'}],
|
||||
'age_wmean': 17.32853078842163,
|
||||
'is_student_probs': [{'prob': 0.8173828125, 'choice': 'true'},
|
||||
{'prob': 0.182373046875, 'choice': 'false'}],
|
||||
'is_student': False,
|
||||
'age': 17,
|
||||
'unit_time': 0.5,
|
||||
'courses': ['CS101'],
|
||||
'trim': None,
|
||||
'color': 'white'}
|
||||
```
|
||||
|
||||
The original [README](https://github.com/1rgs/jsonformer) is includes below.
|
||||
|
||||
# ORIGINAL: Jsonformer: A Bulletproof Way to Generate Structured JSON from Language Models.
|
||||
|
||||
### Problem: Getting models to output structured JSON is hard
|
||||
|
||||
+69
-47
@@ -312,65 +312,87 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'name': 'John Doe',\n",
|
||||
" 'age': 20,\n",
|
||||
" 'age_probs': [{'prob': 0.794921875, 'choice': '12'},\n",
|
||||
" {'prob': 0.068359375, 'choice': '10'},\n",
|
||||
" {'prob': 0.04345703125, 'choice': '16'},\n",
|
||||
" {'prob': 0.03228759765625, 'choice': '14'},\n",
|
||||
" {'prob': 0.0175628662109375, 'choice': '11'},\n",
|
||||
" {'prob': 0.0157318115234375, 'choice': '15'},\n",
|
||||
" {'prob': 0.013671875, 'choice': '20'},\n",
|
||||
" {'prob': 0.006664276123046875, 'choice': '18'},\n",
|
||||
" {'prob': 0.0046539306640625, 'choice': '13'},\n",
|
||||
" {'prob': 0.0018215179443359375, 'choice': '21'},\n",
|
||||
" {'prob': 0.00041294097900390625, 'choice': '17'},\n",
|
||||
" {'prob': 0.00028824806213378906, 'choice': '19'},\n",
|
||||
" {'prob': 0.00014495849609375, 'choice': '22'},\n",
|
||||
" {'prob': 6.955862045288086e-05, 'choice': '23'},\n",
|
||||
" {'prob': 2.968311309814453e-05, 'choice': '25'},\n",
|
||||
" {'prob': 2.8789043426513672e-05, 'choice': '26'},\n",
|
||||
" {'prob': 1.901388168334961e-05, 'choice': '24'},\n",
|
||||
" {'prob': 1.1742115020751953e-05, 'choice': '28'},\n",
|
||||
" {'prob': 1.1920928955078125e-06, 'choice': '27'},\n",
|
||||
" {'prob': 7.748603820800781e-07, 'choice': '29'}],\n",
|
||||
" 'unit_time': 0.01,\n",
|
||||
" 'is_student': True,\n",
|
||||
" 'is_student_probs': [{'prob': 0.8310546875, 'choice': 'true'},\n",
|
||||
" {'prob': 0.1688232421875, 'choice': 'false'}],\n",
|
||||
" 'courses': ['C1'],\n",
|
||||
" 'trim': None,\n",
|
||||
" 'color': 'white'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"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)\n",
|
||||
"\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\"},\n",
|
||||
" \"age\": {\"type\": \"integer\"},\n",
|
||||
" \"name\": {\"type\": \"string\", \"maxLength\": 4},\n",
|
||||
" \"age_probs\": {\"type\": \"choice_probs\", \"enum\": [str(s) for s in range(10, 30)]},\n",
|
||||
" \"unit_time\": {\"type\": \"number\"},\n",
|
||||
" \"is_student\": {\"type\": \"boolean\"},\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",
|
||||
|
||||
@@ -10,7 +10,6 @@ class StringStoppingCriteria(StoppingCriteria):
|
||||
self.tokenizer = tokenizer
|
||||
self.prompt_length = prompt_length
|
||||
self.max_length = max_length
|
||||
print(max_length, ", max_length")
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
@@ -28,7 +27,6 @@ class StringStoppingCriteria(StoppingCriteria):
|
||||
if self.max_length is not None:
|
||||
str_l = len(self.tokenizer.decode(input_ids[0], skip_special_tokens=True))
|
||||
if str_l > self.max_length:
|
||||
print("maxlen", str_l)
|
||||
return True
|
||||
|
||||
return result
|
||||
|
||||
Reference in New Issue
Block a user