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Adding support for Sentence-Transformers 3.3.1 #128

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Adding support for Sentence-Transformers 3.3.1 #128

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54076ec
Adding support for Sentence-Transformers 3.3.1
NoahBPeterson Jan 4, 2025
4565c64
Update InstructorEmbedding version to 1.0.3
NoahBPeterson Jan 4, 2025
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38 changes: 22 additions & 16 deletions InstructorEmbedding/instructor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,13 @@
import json
import os
from collections import OrderedDict
from typing import Union
from typing import Literal, Union

import numpy as np
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Transformer
from sentence_transformers.util import load_file_path, load_dir_path
from torch import Tensor, nn
from tqdm.autonotebook import trange
from transformers import AutoConfig, AutoTokenizer
Expand Down Expand Up @@ -278,6 +279,7 @@ def __init__(
max_seq_length=None,
model_args=None,
cache_dir=None,
backend: Literal['torch', 'onnx', 'openvino'] = 'torch',
tokenizer_args=None,
do_lower_case: bool = False,
tokenizer_name_or_path: Union[str, None] = None,
Expand Down Expand Up @@ -307,7 +309,7 @@ def __init__(
)

if load_model:
self._load_model(self.model_name_or_path, config, cache_dir, **model_args)
self._load_model(self.model_name_or_path, config, cache_dir, **model_args, backend=backend)
self.tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name_or_path
if tokenizer_name_or_path is not None
Expand Down Expand Up @@ -517,7 +519,9 @@ def smart_batching_collate(self, batch):

return batched_input_features, labels

def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=None, trust_remote_code=False):
def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=None,
trust_remote_code=None, local_files_only=None, model_kwargs=None,
tokenizer_kwargs=None, config_kwargs=None):
"""
Loads a full sentence-transformers model
"""
Expand All @@ -527,15 +531,15 @@ def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=
if os.path.isdir(model_path):
model_path = str(model_path)
else:
# If model_path is a Hugging Face repository ID, download the model
download_kwargs = {
"repo_id": model_path,
"revision": revision,
"library_name": "InstructorEmbedding",
"token": token,
"cache_dir": cache_folder,
"tqdm_class": disabled_tqdm,
}
model_path = load_dir_path(
model_path,
directory="*",
token=token,
cache_folder=cache_folder,
revision=revision,
local_files_only=local_files_only,
)
model_path = model_path[:model_path.index("*")]

# Check if the config_sentence_transformers.json file exists (exists since v2 of the framework)
config_sentence_transformers_json_path = os.path.join(
Expand All @@ -562,17 +566,19 @@ def _load_sbert_model(self, model_path, token=None, cache_folder=None, revision=
modules_config = json.load(config_file)

modules = OrderedDict()
module_kwargs = OrderedDict()
for module_config in modules_config:
if module_config["idx"] == 0:
module_class = INSTRUCTORTransformer
elif module_config["idx"] == 1:
module_class = INSTRUCTORPooling
else:
module_class = import_from_string(module_config["type"])
module = module_class.load(os.path.join(model_path, module_config["path"]))
modules[module_config["name"]] = module
module_class = import_from_string(module_config['type'])
module = module_class.load(os.path.join(model_path, module_config['path']))
modules[module_config['name']] = module
module_kwargs[module_config['name']] = module

return modules
return modules, module_kwargs

def encode(
self,
Expand Down
2 changes: 1 addition & 1 deletion setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
setup(
name='InstructorEmbedding',
packages=['InstructorEmbedding'],
version='1.0.2',
version='1.0.3',
license='Apache License 2.0',
description='Text embedding tool',
long_description=readme,
Expand Down