Set up TalkToModel env

[qiaw99]

Intergration

This is an overview how we could intergrate other desired dataset into TTM.

1. Get TTM

git clone git@github.com:dylan-slack/TalkToModel.git

2. Setup env

Get into the TTM directory and run these commands:

conda create -n ttm python=3.9
conda activate ttm

Then you should all dependencies:

pip install -r requirements.txt
pip install datasets

3. Add needed files

Firstly, put the configure file into folder /configs as /configs/boolq.gin

##########################################
# The boolq dataset conversation config
##########################################

# for few shot, e.g., "EleutherAI/gpt-neo-2.7B"
ExplainBot.parsing_model_name = "EleutherAI/gpt-neo-2.7B"


# set skip_prompts to true for quicker startup for finetuned models
# make sure to set to false using few-shot models
ExplainBot.skip_prompts = False

ExplainBot.t5_config = "./parsing/t5/gin_configs/t5-large.gin"
ExplainBot.seed = 0
ExplainBot.background_dataset_file_path = "./data/boolq_train.csv"
ExplainBot.model_file_path = "./data/boolq_model"
ExplainBot.dataset_file_path = "./data/boolq_validation.csv"

ExplainBot.name = "boolq"

ExplainBot.dataset_index_column = "idx"
ExplainBot.target_variable_name = "label"
ExplainBot.categorical_features = None
ExplainBot.numerical_features = None
ExplainBot.remove_underscores = False

ExplainBot.prompt_metric = "cosine"
ExplainBot.prompt_ordering = "ascending"

# Prompt params
Prompts.prompt_cache_size = 1_000_000
Prompts.prompt_cache_location = "./cache/boolq-prompts.pkl"
Prompts.max_values_per_feature = 2
Prompts.sentence_transformer_model_name = "all-mpnet-base-v2"
Prompts.prompt_folder = "./explain/prompts"
Prompts.num_per_knn_prompt_template = 1
Prompts.num_prompt_template = 7

# Explanation Params
Explanation.max_cache_size = 1_000_000

# MegaExplainer Params
MegaExplainer.cache_location = "./cache/boolq-mega-explainer-tabular.pkl"
MegaExplainer.use_selection = False

# Conversation params
Conversation.class_names = {1: "True", 0: "False"}

# Dataset description
DatasetDescription.dataset_objective = "predict to answer yes/no questions based on text passages"
DatasetDescription.dataset_description = "Boolean question answering (yes/no)"
DatasetDescription.model_description = "DistilBERT"

And change the global config files in global_config.gin:

GlobalArgs.config = "./configs/boolq.gin"

Then you should add datasets:

from datasets import load_dataset
val = load_dataset("super_glue", "boolq", split="validation").to_csv('data/boolq_validation.csv')
train = load_dataset("super_glue", "boolq", split="train").to_csv('data/boolq_train.csv')

What's more, you should download the model:
https://huggingface.co/andi611/distilbert-base-uncased-qa-boolq/tree/main. And put it under /configs as ./configs/boolq_model.

Adapt original files:

In /explain/logic.py,

• Add load_hf_model()

@gin.configurable
def load_hf_model(model_id):
    """ Loads a (local) Hugging Face model from a directory containing a pytorch_model.bin file and a config.json file.
    """
    return TransformerModel(model_id)
    # transformers.AutoModel.from_pretrained(model_id)

• Comment load_explanations:

# Load the explanations
# self.load_explanations(background_dataset=background_dataset)

• Change else part from load_model():

else:
    model = load_hf_model(filepath)
    self.conversation.add_var('model', model, 'model')

4. Execution

python flash_app.py

[nfelnlp]

Moved to Wiki