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Python library for Language Model / Finetune using Transformer based models.

Project description

NLP_LIB

The python library for language modeling and fine tuning using Transformer based deep learning models with built-in Thai data set supported.

Features

Lanugage Models Supported

  • Transformer Decoder-only model (Next token predicton objective function)
  • Transformer Encoder-only model (Masked tokens prediction objective function)

Fine Tuning Models Supported

  • Sequence-to-Sequence Model
  • Multi Class Classification
  • Multi Label Classification

Built-in Data Set Supported

  • NECTEC BEST2010 for Language Model
  • Thailand Wikipedia Dump for Langauge Model
  • NECTEC BEST2010 for Topic Classification
  • Truevoice for Intention Detection
  • Wisesight for Sentiment Analysis
  • Wongnai for Rating Prediction

Build-in Input / Output Transformation

  • Full word dictionary
  • Bi-gram dictionary
  • Sentencepiece dictionary

Other Features

  • Automatic multi-GPUs detection and training support (Data Parallel)
  • Automatic state saving and resume training
  • Automatic saving best model and last model during training
  • Automatic generate Tensorboard log
  • Sequence generation from language model using ARGMAX, BEAM Search
  • Support initialization from trained language model weights in fine tuning
  • Modularized and fully extensible

Library Usages

python3 -m NLP_LIB <model_name | model_json_path> <operation> <extra_params>
  • model_name: Predefined model name shipped with the library (See appendix A. for list of predefined models)
  • model_json_path: JSON Configuration File path of the model (See appendix B. JSON file format)
  • operation: train | predict | generate - default is train (See example section for how to use "generate" mode)

Examples of running the training process

Train 6 layers of transformer decoder-only model

python3 -m NLP_LIB tf6-dec

Finetune 4 layers of transformer encoder-only with sentencepiece dict model on truevoice data

python3 -m NLP_LIB tf4-enc-sp+truevoice

Run prediction on input data file

python3 -m NLP_LIB tf4-enc-sp+truevoice predict file:input_data.txt

Run prediction on input string

python3 -m NLP_LIB tf4-dec-bigram+best2010 predict str:This,is,input,text

Run sequence generation for 20 tokens using BEAM search on 3 best prediction sequences

python3 -m NLP_LIB tf6-dec generate:20:beam3 str:This,is,seed,text

APPENDIX A) List of predefined models

For language model:

tf<N>-<Arch>-<Dict> : Transformer models
  • N : Number of transformer layers, support 2, 4, 6 and 12. Default is 6.
  • Arch: Architecture of language model, support "enc" and "dec" for encoder-only and decoder-only.
  • Dict: Data transformation, support "full", "bigram" and "sp" for full word dict, bigram dict and sentencepiece dict. Default is "full"

Examples:

tf-dec
tf6-dec
tf4-enc-full
tf12-dec-sp
tf2-enc-bigram

For fine tuning model:

tf<N>-<Arch>-<Dict>+<Finetune Data> : Transformer models
  • N : Number of transformer layers, support 2, 4, 6 and 12. Default is 6.
  • Arch: Architecture of language model, support "enc" and "dec" for encoder-only and decoder-only.
  • Dict: Data transformation, support "full", "bigram" and "sp" for full word dict, bigram dict and sentencepiece dict. Default is "full"
  • Finetune Data: Fine tuning data set, support "best2010", "truevoice", "wongnai" and "wisesight"

Examples:

tf-dec+best2010
tf6-dec+truevoice
tf4-enc-full+wongnai
tf12-dec-sp+wisesight

APPENDIX B) JSON Configuration File Format

This file defines how to run the model training. The model training run is defined by 5 components below:

  • Model : Model architecture to be used
  • Dataset : Dataset to be used
  • Input / Output Transformation : How to encode / decode input and output data
  • Callbacks : List of additional flow need to be run in training loop
  • Execution : Training processes to be used, for example what optimizer, how many epoch

The JSON file needs to supply configuration of each component, the overall format is shown below:

{
  "model": {
    "class": <CLASS NAME OF MODEL>,
    "config": {
      <CONFIGURATIONS OF THE MODEL>
    }
  },
  "dataset": {
    "class": <CLASS NAME OF DATA SET>,
    "config": {
      <CONFIGURATIONS OF THE DATA SET>
    }
  },
  "input_transform": {
    "class": <CLASS NAME OF INPUT TRANSFORMATION>,
    "config": {
      <CONFIGURATIONS OF THE INPUT TRANFORMATION>
    }
  },
  "output_transform": {
    "class": <CLASS NAME OF OUTPUT TRANSFORMATION>,
    "config": {
      <CONFIGURATIONS OF THE OUTPUT TRANFORMATION>
    }
  },
  "callbacks": [
    .... [MULTIPLE CALLBACK HOOKS] ....
    {
      "class": <CLASS NAME OF CALLBACK HOOKS>,
      "config": {
        <CONFIGURATIONS OF THE CALLBACK>
      }
    }
  ],
  "execution": {
    "config": {
      <CONFIGURATIONS OF THE TRAINING PROCESS>
    }
  }
}

Overall is that the configuration of each module requires class name of the module and also configurations for them. The required / optional configurations of each module are depended on module class so you have to read document for each module class to find out how to config them. The class name of each module is used to look up for implementation of the module in the following directories:

  • model => ./models
  • dataset => ./datasets
  • input / output transformations => ./transforms
  • callbacks => ./callbacks

You can implement new module by putting module python class in above directories and the library will be able to resolve for implementation when it finds class name in JSON configuration file.

Below is example of JSON configuration file for training 12 layers of transformer decoder-only model with sentencepiece dictionary data transformation and dynamic learning rate on THWIKI data set:

{
  "model": {
    "class": "TransformerDecoderOnlyWrapper",
    "config": {
      "len_limit": 256,
      "d_model": 512,
      "d_inner_hid": 2048,
      "n_head": 8,
      "d_k": 512,
      "d_v": 512,
      "layers": 12,
      "dropout": 0.1,
      "share_word_emb": true,
      "max_input_length": 256,
      "cached_data_dir": "_cache_"
    }
  },
  "dataset": {
    "class": "THWIKILMDatasetWrapper",
    "config": {
      "base_data_dir": "_tmp_"
    }
  },
  "input_transform": {
    "class": "SentencePieceDictionaryWrapper",
    "config": {
      "column_id": 0,
      "max_dict_size" : 15000,
      "mask_last_token": false
    }
  },
  "output_transform": {
    "class": "SentencePieceDictionaryWrapper",
    "config": {
      "column_id": 1,
      "max_dict_size" : 15000
    }
  },
  "callbacks": [
    {
      "class": "DynamicLearningRateWrapper",
      "config": {
        "d_model": 512,
        "warmup": 50000,
        "scale": 0.5
      }
    }
  ],
  "execution": {
    "config": {
      "optimizer": "adam",
      "optimizer_params": [0.1, 0.9, 0.997, 1e-9],
      "batch_size": 32,
      "epochs": 60,
      "watch_metric": "val_acc",
      "output_dir": "_outputs_/thwikilm_tfbase12_dec_s2_sp",
      "save_weight_history": false,
      "resume_if_possible": true,
      "multi_gpu": false
    }
  }
}

Below is another example of using the trained model above to finetune on TRUEVOICE data set. Note that we use "SequenceTransferLearningWrapper" model class, which accept configuration of language model to be used as an encoder and also the original data set configuration used to pre-train the encoder model:

{
  "model": {
    "class": "SequenceTransferLearningWrapper",
    "config": {
      "output_class_num": 8,
      "encoder_checkpoint": "_outputs_/thwikilm_tfbase12_dec_s2_sp/checkpoint/best_weight.h5",
      "train_encoder": true,
      "max_input_length": 256,
      "drop_out": 0.4,
      "cached_data_dir": "_cache_",

      "encoder_model": {
        "class": "TransformerDecoderOnlyWrapper",
        "config": {
          "len_limit": 256,
          "d_model": 512,
          "d_inner_hid": 2048,
          "n_head": 8,
          "d_k": 512,
          "d_v": 512,
          "layers": 12,
          "dropout": 0.1,
          "share_word_emb": true,
          "max_input_length": 256,
          "cached_data_dir": "_cache_"
        }
      },

      "encoder_dict_dataset": {
        "class": "THWIKILMDatasetWrapper",
        "config": {
          "base_data_dir": "_tmp_"
        }
      }

    }
  },
  "dataset": {
    "class": "TruevoiceDatasetWrapper",
    "config": {
      "base_data_dir": "_tmp_"
    }
  },
  "input_transform": {
    "class": "SentencePieceDictionaryWrapper",
    "config": {
      "column_id": 0,
      "max_dict_size" : 15000,
      "clf_pos_offset": -1,
      "clf_id": 3,
      "mask_last_token": false
    }
  },  
  "output_transform": {
    "class": "SingleClassTransformWrapper",
    "config": {
      "column_id": 1
    }
  },
  "callbacks": [
    {
      "class": "DynamicLearningRateWrapper",
      "config": {
        "d_model": 512,
        "warmup": 50000,
        "scale" : 0.25
      }
    }
  ],
  "execution": {
    "config": {
      "optimizer": "adam",
      "optimizer_params": [0.0001, 0.9, 0.98, 1e-9],
      "batch_size": 32,
      "epochs": 30,
      "watch_metric": "val_acc",
      "output_dir": "_outputs_/finetune_thwikilm_tfbase12_dec_s2s_sp_truevoice",
      "save_weight_history": false,
      "resume_if_possible": true
    }
  }
}

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