Conversational AI Model

Data Formats

Data format follows the Facebook Persona-Chat format. A JSON formatted version by Hugging Face is found here. The JSON file is directly compatible with this library (and it will be automatically downloaded and used if no dataset is specified).

Each entry in personachat is a dict with two keys personality and utterances, the dataset is a list of entries.

  • personality: list of strings containing the personality of the agent
  • utterances: list of dictionaries, each of which has two keys which are lists of strings.
    • candidates: [next_utterance_candidate_1, …, next_utterance_candidate_19] The last candidate is the ground truth response observed in the conversational data
    • history: [dialog_turn_0, … dialog_turn N], where N is an odd number since the other user starts every conversation.

Preprocessing:

  • Spaces before periods at end of sentences
  • everything lowercase

Example train data:

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[
    {
        "personality": [
            "i like computers .",
            "i like reading books .",
            "i like talking to chatbots .",
            "i love listening to classical music ."
        ],
        "utterances": [
            {
                "candidates": [
                    "i try to wear all black every day . it makes me feel comfortable .",
                    "well nursing stresses you out so i wish luck with sister",
                    "yeah just want to pick up nba nfl getting old",
                    "i really like celine dion . what about you ?",
                    "no . i live near farms .",
                    "mother taught me to cook ! we are looking for an exterminator .",
                    "i enjoy romantic movie . what is your favorite season ? mine is summer .",
                    "editing photos takes a lot of work .",
                    "you must be very fast . hunting is one of my favorite hobbies .",
                    "hi there . i'm feeling great! how about you ?"
                ],
                "history": [
                    "hi , how are you ?"
                ]
            },
            {
                "candidates": [
                    "i have trouble getting along with family .",
                    "i live in texas , what kind of stuff do you do in ",
                    "toronto ?",
                    "that's so unique ! veganism and line dancing usually don't mix !",
                    "no , it isn't that big . do you travel a lot",
                    "that's because they are real ; what do you do for work ?",
                    "i am lazy all day lol . my mom wants me to get a job and move out",
                    "i was born on arbor day , so plant a tree in my name",
                    "okay , i should not tell you , its against the rules ",
                    "i like to talk to chatbots too ! do you know why ? ."
                ],
                "history": [
                    "hi , how are you ?",
                    "hi there . i'm feeling great! how about you ?",
                    "not bad ! i am trying out this chatbot ."
                ]
            },
            {
                "candidates": [
                    "ll something like that . do you play games ?",
                    "does anything give you relief ? i hate taking medicine for mine .",
                    "i decorate cakes at a local bakery ! and you ?",
                    "do you eat lots of meat",
                    "i am so weird that i like to collect people and cats",
                    "how are your typing skills ?",
                    "yeah . i am headed to the gym in a bit to weight lift .",
                    "yeah you have plenty of time",
                    "metal is my favorite , but i can accept that people listen to country . haha",
                    "that's why you desire to be controlled . let me control you person one .",
                    "two dogs they are the best , how about you ?",
                    "you do art ? what kind of art do you do ?",
                    "i love watching baseball outdoors on sunny days .",
                    "oh i see . do you ever think about moving ? i do , it is what i want .",
                    "because i am a chatbot too, silly !"
                ],
                "history": [
                    "hi , how are you ?",
                    "hi there . i'm feeling great! how about you ?",
                    "not bad ! i am trying out this chatbot .",
                    "i like to talk to chatbots too ! do you know why ? .",
                    "no clue, why don't you tell me ?"
                ]
            }
        ]
    }
]

ConvAIModel

class simpletransformers.conv_ai.ConvAIModel ( model_type, model_name, args=None, use_cuda=True, cuda_device=-1, **kwargs) This class is used to build Conversational AI.

Class attributes

  • tokenizer: The tokenizer to be used.
  • model: The model to be used. model_name: Default Transformer model name or path to Transformer model file (pytorch_model.bin).
  • device: The device on which the model will be trained and evaluated.
  • results: A python dict of past evaluation results for the TransformerModel object.
  • args: A python dict of arguments used for training and evaluation.

Parameters

  • model_type: (required) str - The type of model to use.
  • model_name: (required) str - The exact model to use. Could be a pretrained model name or path to a directory containing a model. See Current Pretrained Models for all available models.
  • args: (optional) python dict - A dictionary containing any settings that should be overwritten from the default values.
  • use_cuda: (optional) bool - Default = True. Flag used to indicate whether CUDA should be used.
  • cuda_device: (optional) int - Default = -1. Used to specify which GPU should be used.

class methods train_model(self, train_file=None, output_dir=None, show_running_loss=True, args=None, eval_file=None)

Trains the model using ‘train_file’

Args:

  • train_df: ath to JSON file containing training data. The model will be trained on this file. output_dir: The directory where model files will be saved. If not given, self.args[‘output_dir’] will be used.

  • output_dir (optional): The directory where model files will be saved. If not given, self.args[‘output_dir’] will be used.

  • show_running_loss (Optional): Set to False to prevent training loss being printed.

  • args (optional): Optional changes to the args dict of the model. Any changes made will persist for the model.

  • eval_file (optional): Evaluation data against which evaluation will be performed when evaluate_during_training is enabled. If not given when evaluate_during_training is enabled, the evaluation data from PERSONA-CHAT will be used.

Returns:

  • None

eval_model(self, eval_file, output_dir=None, verbose=True, silent=False)

Evaluates the model on eval_file. Saves results to output_dir.

Args:

  • eval_file: Path to JSON file containing evaluation data. The model will be evaluated on this file. If not given, eval dataset from PERSONA-CHAT will be used.

  • output_dir: The directory where model files will be saved. If not given, self.args[‘output_dir’] will be used.

  • verbose: If verbose, results will be printed to the console on completion of evaluation.

  • silent: If silent, tqdm progress bars will be hidden.

Returns:

  • result: Dictionary containing evaluation results. (correct, similar, incorrect)

  • text: A dictionary containing the 3 dictionaries correct_text, similar_text (the predicted answer is a substring of the correct answer or vise versa), incorrect_text.

interact(self, personality=None)

Interact with a model in the terminal.

Args:

  • personality (optional): A list of sentences that the model will use to build a personality. If not given, a random personality from PERSONA-CHAT will be picked.
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model.interact(
    personality=[
        "i like computers .",
        "i like reading books .",
        "i love classical music .",
        "i am very social ."
    ]
)

Returns:

  • None

train(self, train_dataloader, output_dir, show_running_loss=True, eval_dataloader=None, verbose=verbose)

Trains the model on train_dataset. Utility function to be used by the train_model() method. Not intended to be used directly.

evaluate(self, eval_file, output_dir, verbose=True, silent=False)

Evaluates the model on eval_file. Utility function to be used by the eval_model() method. Not intended to be used directly

load_and_cache_examples(self, dataset_path=None, evaluate=False, no_cache=False, verbose=True, silent=False)

Loads, tokenizes, and prepares data for training and/or evaluation. Utility function for train() and eval() methods. Not intended to be used directly

Updated: