old_contents: """
  This examples measures the inference speed of a certain model
  
  Usage:
  python evaluation_inference_speed.py
  OR
  python evaluation_inference_speed.py model_name
  """
  from sentence_transformers import SentenceTransformer, util
  import sys
  import os
  import time
  import torch
  import gzip
  import csv
  
  #Limit torch to 4 threads
  torch.set_num_threads(4)
  
  
  model_name = sys.argv[1] if len(sys.argv) > 1 else 'bert-base-nli-mean-tokens'
  
  # Load a named sentence model (based on BERT). This will download the model from our server.
  # Alternatively, you can also pass a filepath to SentenceTransformer()
  model = SentenceTransformer(model_name)
  
  
  nli_dataset_path = 'datasets/AllNLI.tsv.gz'
  sentences = set()
  max_sentences = 100000
  
  
  #Download datasets if needed
  if not os.path.exists(nli_dataset_path):
      util.http_get('https://sbert.net/datasets/AllNLI.tsv.gz', nli_dataset_path)
  
  with gzip.open(nli_dataset_path, 'rt', encoding='utf8') as fIn:
      reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
      for row in reader:
          sentences.add(row['sentence1'])
          if len(sentences) >= max_sentences:
              break
  
  sentences = list(sentences)
  print("Model Name:", model_name)
  print("Number of sentences:", len(sentences))
  
  for i in range(3):
      print("Run", i)
      start_time = time.time()
      emb = model.encode(sentences, num_workers=2, batch_size=32)
      end_time = time.time()
      diff_time = end_time - start_time
      print("Done after {:.2f} seconds".format(diff_time))
      print("Speed: {:.2f} sentences / second".format(len(sentences) / diff_time))
      print("=====")
new_contents: """
  This examples measures the inference speed of a certain model
  
  Usage:
  python evaluation_inference_speed.py
  OR
  python evaluation_inference_speed.py model_name
  """
  from sentence_transformers import SentenceTransformer, util
  import sys
  import os
  import time
  import torch
  import gzip
  import csv
  
  #Limit torch to 4 threads
  torch.set_num_threads(4)
  
  
  model_name = sys.argv[1] if len(sys.argv) > 1 else 'bert-base-nli-mean-tokens'
  
  # Load a named sentence model (based on BERT). This will download the model from our server.
  # Alternatively, you can also pass a filepath to SentenceTransformer()
  model = SentenceTransformer(model_name)
  
  
  nli_dataset_path = 'datasets/AllNLI.tsv.gz'
  sentences = set()
  max_sentences = 100000
  
  
  #Download datasets if needed
  if not os.path.exists(nli_dataset_path):
      util.http_get('https://sbert.net/datasets/AllNLI.tsv.gz', nli_dataset_path)
  
  with gzip.open(nli_dataset_path, 'rt', encoding='utf8') as fIn:
      reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
      for row in reader:
          sentences.add(row['sentence1'])
          if len(sentences) >= max_sentences:
              break
  
  sentences = list(sentences)
  print("Model Name:", model_name)
  print("Number of sentences:", len(sentences))
  
  for i in range(3):
      print("Run", i)
      start_time = time.time()
      emb = model.encode(sentences, batch_size=32)
      end_time = time.time()
      diff_time = end_time - start_time
      print("Done after {:.2f} seconds".format(diff_time))
      print("Speed: {:.2f} sentences / second".format(len(sentences) / diff_time))
      print("=====")
  
subject: Update evaluation_inference_speed.py