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st-codesearch-distilroberta-base

Sentence Similarity
sentence-transformers
PyTorch
roberta
feature-extraction
text-embeddings-inference
Inference Endpoints
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st-codesearch-distilroberta-base
File size: 3,837 Bytes 
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import math
from sentence_transformers import models, losses, datasets
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import sys
import os
import gzip
import csv
from MultiDatasetDataLoader import MultiDatasetDataLoader
from shutil import copyfile
import json
import argparse

#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
                    datefmt='%Y-%m-%d %H:%M:%S',
                    level=logging.INFO,
                    handlers=[LoggingHandler()])
#### /print debug information to stdout


#model_name = 'distilroberta-base' 
#batch_size_pairs = 200
#batch_size_triplets = 200 
#steps_per_epoch = 10000

parser = argparse.ArgumentParser()
parser.add_argument('--model', default='nreimers/MiniLM-L6-H384-uncased')
parser.add_argument('--steps', type=int, default=2000)
parser.add_argument('--batch_size_pairs', type=int, default=256)
parser.add_argument('--batch_size_triplets', type=int, default=256)
parser.add_argument('--data', nargs='+', default=[])
parser.add_argument('--name')
args = parser.parse_args()


model_name = args.model #'nreimers/MiniLM-L6-H384-uncased'
batch_size_pairs = args.batch_size_pairs #256
batch_size_triplets = args.batch_size_triplets #256 
steps_per_epoch = args.steps #2000

num_epochs = 1
max_seq_length = 128
use_amp = True
warmup_steps = 500

#####

output_path = 'output/training_data_benchmark-{}-norm-{}'.format(model_name.replace("/", "-"), args.name)
logging.info("Output: "+output_path)
if os.path.exists(output_path):
    exit()


# Write train script to output path
os.makedirs(output_path, exist_ok=True)

train_script_path = os.path.join(output_path, 'train_script.py')
copyfile(__file__, train_script_path)
with open(train_script_path, 'a') as fOut:
    fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))

## SentenceTransformer model
word_embedding_model = models.Transformer(model_name, max_seq_length=max_seq_length)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
norm = models.Normalize()
model = SentenceTransformer(modules=[word_embedding_model, pooling_model, norm])

datasets = []
for filepath in args.data:
    filepath = filepath.strip()
    dataset = []
    

    with gzip.open(filepath, 'rt', encoding='utf8') as fIn:
        for line in fIn:
            data = json.loads(line.strip())
            
            if not isinstance(data, dict):
                data = {'guid': None, 'texts': data}
           
            dataset.append(InputExample(guid=data.get('guid', None), texts=data['texts']))
            if len(dataset) >= (steps_per_epoch * batch_size_pairs * 2):
                break

    datasets.append(dataset)
    logging.info("{}: {}".format(filepath, len(dataset)))



train_dataloader = MultiDatasetDataLoader(datasets, batch_size_pairs=batch_size_pairs, batch_size_triplets=batch_size_triplets, random_batch_fraction=0.25)


# Our training loss
train_loss = losses.MultipleNegativesRankingLoss(model, scale=20, similarity_fct=util.dot_score)



#Read STSbenchmark dataset and use it as development set

# Configure the training
logging.info("Warmup-steps: {}".format(warmup_steps))

# Train the model
model.fit(train_objectives=[(train_dataloader, train_loss)],
          evaluator=None,
          epochs=1,
          warmup_steps=warmup_steps,
          steps_per_epoch=steps_per_epoch,
          scheduler='warmupconstant',
          use_amp=use_amp
          )


model.save(output_path)

# Script was called via:
#python training_data_benchmark_norm_cos.py --name codesearch-full --model distilroberta-base --steps 10000 --data data/codesearchnet.jsonl.gz