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Model: tifa-benchmark/llama2_tifa_question_generation Source: Original Platform
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README.md
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---
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license: apache-2.0
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inference: true
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widget:
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- text: "<s>[INST] <<SYS>>\nGiven an image description, generate one or two multiple-choice questions that verifies if the image description is correct.\nClassify each concept into a type (object, human, animal, food, activity, attribute, counting, color, material, spatial, location, shape, other), and then generate a question for each type.\n\n<</SYS>>\n\nDescription: a blue rabbit and a red plane [/INST] Entities:"
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pipeline_tag: text-generation
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tags:
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- text-generation-inference
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- llama2
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- text-to-image
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datasets:
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- TIFA
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language:
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- en
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---
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Project page: <https://tifa-benchmark.github.io/>
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This is the text parsing and question generation model for the ICCV 2023 paper [TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering](https://arxiv.org/abs/2303.11897)
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We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image.
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Specifically, this fine-tuned LLaMA 2 model is the substitute for the GPT-3 model in the paper. It can parse an arbitrary prompt into visual entities, attributes, relations, etc. and generate question-answer tuples for each of them. See examples below.
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# QuickStart
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All codes are from <https://github.com/Yushi-Hu/tifa>. Clone this repo to easily use this model together with other modules (e.g. VQA) provided in TIFA.
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Please follow the prompt format, which will give the best performance.
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```python
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import torch
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import transformers
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# prepare the LLaMA 2 model
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model_name = "tifa-benchmark/llama2_tifa_question_generation"
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_name,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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# formating prompt following LLaMA 2 style
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def create_qg_prompt(caption):
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INTRO_BLURB = "Given an image description, generate one or two multiple-choice questions that verifies if the image description is correct.\nClassify each concept into a type (object, human, animal, food, activity, attribute, counting, color, material, spatial, location, shape, other), and then generate a question for each type.\n"
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formated_prompt = f"<s>[INST] <<SYS>>\n{INTRO_BLURB}\n<</SYS>>\n\n"
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formated_prompt += f"Description: {caption} [/INST] Entities:"
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return formated_prompt
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test_caption = "a blue rabbit and a red plane"
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# create prompt
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prompt = create_qg_prompt(text_caption)
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# text completion
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sequences = pipeline(
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prompt, do_sample=False, num_beams=5, num_return_sequences=1, max_length=512)
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output = sequences[0]['generated_text'][len(prompt):]
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output = output.split('\n\n')[0]
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# output
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print(output)
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#### Expected output ###
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# rabbit, plane
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# Activites:
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# Colors: blue, red
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# Counting:
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# Other attributes:
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# About rabbit (animal):
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# Q: is this a rabbit?
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# Choices: yes, no
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# A: yes
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# About rabbit (animal):
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# Q: what animal is in the picture?
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# Choices: rabbit, dog, cat, fish
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# A: rabbit
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# About plane (object):
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# Q: is this a plane?
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# Choices: yes, no
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# A: yes
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# About plane (object):
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# Q: what type of vehicle is this?
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# Choices: plane, car, motorcycle, bus
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# A: plane
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# About blue (color):
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# Q: is the rabbit blue?
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# Choices: yes, no
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# A: yes
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# About blue (color):
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# Q: what color is the rabbit?
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# Choices: blue, red, yellow, green
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# A: blue
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# About red (color):
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# Q: is the plane red?
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# Choices: yes, no
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# A: yes
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# About red (color):
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# Q: what color is the plane?
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# Choices: red, blue, yellow, green
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# A: red
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```
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# Use this LM under tifascore package
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tifascore provides extra functions to parse this output etc. First install tifascore according to <https://github.com/Yushi-Hu/tifa>. Then the usage is below
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```python
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from tifascore import get_llama2_pipeline, get_llama2_question_and_answers
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pipeline = get_llama2_pipeline("tifa-benchmark/llama2_tifa_question_generation")
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print(get_llama2_question_and_answers(pipeline, "a blue rabbit and a red plane"))
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#### Expected output ###
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# [{'caption': 'a blue rabbit and a red plane', 'element': 'rabbit', 'question': 'what animal is in the picture?', 'choices': ['rabbit', 'dog', 'cat', 'fish'], 'answer': 'rabbit', 'element_type': 'animal/human'}, {'caption': 'a blue rabbit and a red plane', 'element': 'plane', 'question': 'is this a plane?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'object'}, {'caption': 'a blue rabbit and a red plane', 'element': 'plane', 'question': 'what type of vehicle is this?', 'choices': ['plane', 'car', 'motorcycle', 'bus'], 'answer': 'plane', 'element_type': 'object'}, {'caption': 'a blue rabbit and a red plane', 'element': 'blue', 'question': 'is the rabbit blue?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'blue', 'question': 'what color is the rabbit?', 'choices': ['blue', 'red', 'yellow', 'green'], 'answer': 'blue', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'red', 'question': 'is the plane red?', 'choices': ['yes', 'no'], 'answer': 'yes', 'element_type': 'color'}, {'caption': 'a blue rabbit and a red plane', 'element': 'red', 'question': 'what color is the plane?', 'choices': ['red', 'blue', 'yellow', 'green'], 'answer': 'red', 'element_type': 'color'}]
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```
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## Bibtex
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```
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@article{hu2023tifa,
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title={Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering},
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author={Hu, Yushi and Liu, Benlin and Kasai, Jungo and Wang, Yizhong and Ostendorf, Mari and Krishna, Ranjay and Smith, Noah A},
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journal={arXiv preprint arXiv:2303.11897},
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year={2023}
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}
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```
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config.json
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|
||||
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|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93391
tokenizer.json
Normal file
93391
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user