nbroad / ESG-BERT

pip install torchserve torch-model-archiver
 
pip install torchvision
 
pip install transformers
 

Next up, we'll set up the handler script. It is a basic handler for text classification that can be improved upon. Save this script as "handler.py" in your directory. [1]

 
from abc import ABC
 
import json
 
import logging
 
import os
 
import torch
 
from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
from ts.torch_handler.base_handler import BaseHandler
 
logger = logging.getLogger(__name__)
 
class TransformersClassifierHandler(BaseHandler, ABC):
 
   """
 
   Transformers text classifier handler class. This handler takes a text (string) and
 
   as input and returns the classification text based on the serialized transformers checkpoint.
 
   """
 
   def __init__(self):
 
       super(TransformersClassifierHandler, self).__init__()
 
       self.initialized = False
 
def initialize(self, ctx):
 
       self.manifest = ctx.manifest
 
properties = ctx.system_properties
 
       model_dir = properties.get("model_dir")
 
       self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
 
# Read model serialize/pt file
 
       self.model = AutoModelForSequenceClassification.from_pretrained(model_dir)
 
       self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
 
self.model.to(self.device)
 
       self.model.eval()
 
logger.debug('Transformer model from path {0} loaded successfully'.format(model_dir))
 
# Read the mapping file, index to object name
 
       mapping_file_path = os.path.join(model_dir, "index_to_name.json")
 
if os.path.isfile(mapping_file_path):
 
           with open(mapping_file_path) as f:
 
               self.mapping = json.load(f)
 
       else:
 
           logger.warning('Missing the index_to_name.json file. Inference output will not include class name.')
 
self.initialized = True
 
def preprocess(self, data):
 
       """ Very basic preprocessing code - only tokenizes.
 
           Extend with your own preprocessing steps as needed.
 
       """
 
       text = data[0].get("data")
 
       if text is None:
 
           text = data[0].get("body")
 
       sentences = text.decode('utf-8')
 
       logger.info("Received text: '%s'", sentences)
 
inputs = self.tokenizer.encode_plus(
 
           sentences,
 
           add_special_tokens=True,
 
           return_tensors="pt"
 
       )
 
       return inputs
 
def inference(self, inputs):
 
       """
 
       Predict the class of a text using a trained transformer model.
 
       """
 
       # NOTE: This makes the assumption that your model expects text to be tokenized 
 
       # with "input_ids" and "token_type_ids" - which is true for some popular transformer models, e.g. bert.
 
       # If your transformer model expects different tokenization, adapt this code to suit
 
       # its expected input format.
 
       prediction = self.model(
 
           inputs['input_ids'].to(self.device),
 
           token_type_ids=inputs['token_type_ids'].to(self.device)
 
       )[0].argmax().item()
 
       logger.info("Model predicted: '%s'", prediction)
 
if self.mapping:
 
           prediction = self.mapping[str(prediction)]
 
return [prediction]
 
def postprocess(self, inference_output):
 
       # TODO: Add any needed post-processing of the model predictions here
 
       return inference_output
 
_service = TransformersClassifierHandler()
 
def handle(data, context):
 
   try:
 
       if not _service.initialized:
 
           _service.initialize(context)
 
if data is None:
 
           return None
 
data = _service.preprocess(data)
 
       data = _service.inference(data)
 
       data = _service.postprocess(data)
 
return data
 
   except Exception as e:
 
       raise e
 
 
 

TorcheServe uses a format called MAR (Model Archive). We can convert our PyTorch model to a .mar file using this command:

 
torch-model-archiver --model-name "bert" --version 1.0 --serialized-file ./bert_model/pytorch_model.bin --extra-files "./bert_model/config.json,./bert_model/vocab.txt" --handler "./handler.py"
 

Move the .mar file into a new directory:

 
mkdir model_store && mv bert.mar model_store
 

Finally, we can start TorchServe using the command:

 
torchserve --start --model-store model_store --models bert=bert.mar
 

We can now query the model from another terminal window using the Inference API. We pass a text file containing text that the model will try to classify.

 
curl -X POST http://127.0.0.1:8080/predictions/bert -T predict.txt
 

This returns a label number which correlates to a textual label. This is stored in the label_dict.txt dictionary file.

 
__label__Business_Ethics :  0
 
__label__Data_Security :  1
 
__label__Access_And_Affordability :  2
 
__label__Business_Model_Resilience :  3
 
__label__Competitive_Behavior :  4
 
__label__Critical_Incident_Risk_Management :  5
 
__label__Customer_Welfare :  6
 
__label__Director_Removal :  7
 
__label__Employee_Engagement_Inclusion_And_Diversity :  8
 
__label__Employee_Health_And_Safety :  9
 
__label__Human_Rights_And_Community_Relations :  10
 
__label__Labor_Practices :  11
 
__label__Management_Of_Legal_And_Regulatory_Framework :  12
 
__label__Physical_Impacts_Of_Climate_Change :  13
 
__label__Product_Quality_And_Safety :  14
 
__label__Product_Design_And_Lifecycle_Management :  15
 
__label__Selling_Practices_And_Product_Labeling :  16
 
__label__Supply_Chain_Management :  17
 
__label__Systemic_Risk_Management :  18
 
__label__Waste_And_Hazardous_Materials_Management :  19
 
__label__Water_And_Wastewater_Management :  20
 
__label__Air_Quality :  21
 
__label__Customer_Privacy :  22
 
__label__Ecological_Impacts :  23
 
__label__Energy_Management :  24
 
__label__GHG_Emissions :  25
 

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