Rasa course, Rasa training, Rasa interview, Hugging Face bert-base-chinese use of Rasa practical series
Hugging Face
Build the AI community of the future to build, train, and deploy state-of-the-art models powered by reference open source in machine learning.
Model downloadbert-base-chinese
Download address https://huggingface.co/models?sort=downloads&search=bert-base-chineseDownload
model files
If it cannot be downloaded due to network reasons, readers can also download Tensorflow's bert-base-cased model on the network disk.
Link: https://pan.baidu.com/s/1KyAUdEdEXi3v1-HyNajNQg
Extraction code: 4xd0
The test code of the Tensorflow model, the model is automatically downloaded and saved to 'C:\Users\admin/.cache\huggingface\transformers'.
# -*- coding: utf-8 -*-
from transformers import AutoTokenizer, TFAutoModel
model_name="bert-base-chinese"
text = "this is a test"
tokenizer = AutoTokenizer.from_pretrained(model_name)
text_tensor = tokenizer.encode(text, return_tensors="tf")
print(text_tensor)
model = TFAutoModel.from_pretrained(model_name)
output = model(text_tensor) #
print(output)
In the E:\anaconda3\envs\installingrasa\Lib\site-packages\transformers\file_utils.py code,
you can also modify the default cache address of Tensorflow to the specified directory: (The directory saved in this article is D:\2022_NOC_AI_RASA_NEW\bert- base-cased)
results:
The file_utils.py code of the tensorflow framework
def cached_path(
url_or_filename,
cache_dir=None,
force_download=False,
proxies=None,
resume_download=False,
user_agent: Union[Dict, str, None] = None,
extract_compressed_file=False,
force_extract=False,
use_auth_token: Union[bool, str, None] = None,
local_files_only=False,
) -> Optional[str]:
"""
Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file
and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and
then return the path
Args:
cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
force_download: if True, re-download the file even if it's already cached in the cache dir.
resume_download: if True, resume the download if incompletely received file is found.
user_agent: Optional string or dict that will be appended to the user-agent on remote requests.
use_auth_token: Optional string or boolean to use as Bearer token for remote files. If True,
will get token from ~/.huggingface.
extract_compressed_file: if True and the path point to a zip or tar file, extract the compressed
file in a folder along the archive.
force_extract: if True when extract_compressed_file is True and the archive was already extracted,
re-extract the archive and override the folder where it was extracted.
Return:
Local path (string) of file or if networking is off, last version of file cached on disk.
Raises:
In case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
"""
if cache_dir is None:
# cache_dir = TRANSFORMERS_CACHE
cache_dir = "D:\\2022_NOC_AI_RASA_NEW\\bert-base-cased"
if isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
if is_remote_url(url_or_filename):
# URL, so get it from the cache (downloading if necessary)
output_path = get_from_cache(
url_or_filename,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
user_agent=user_agent,
use_auth_token=use_auth_token,
local_files_only=local_files_only,
)
elif os.path.exists(url_or_filename):
# File, and it exists.
output_path = url_or_filename
elif urlparse(url_or_filename).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError(f"file {
url_or_filename} not found")
else:
# Something unknown
raise ValueError(f"unable to parse {
url_or_filename} as a URL or as a local path")
if extract_compressed_file:
if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
output_dir, output_file = os.path.split(output_path)
output_extract_dir_name = output_file.replace(".", "-") + "-extracted"
output_path_extracted = os.path.join(output_dir, output_extract_dir_name)
if os.path.isdir(output_path_extracted) and os.listdir(output_path_extracted) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
lock_path = output_path + ".lock"
with FileLock(lock_path):
shutil.rmtree(output_path_extracted, ignore_errors=True)
os.makedirs(output_path_extracted)
if is_zipfile(output_path):
with ZipFile(output_path, "r") as zip_file:
zip_file.extractall(output_path_extracted)
zip_file.close()
elif tarfile.is_tarfile(output_path):
tar_file = tarfile.open(output_path)
tar_file.extractall(output_path_extracted)
tar_file.close()
else:
raise EnvironmentError(f"Archive format of {
output_path} could not be identified")
return output_path_extracted
return output_path
Transformer Flavor
recipe: default.v1
language: zh
pipeline:
# - name: JiebaTokenizer
- name: components.custom_jieba_tokenizer.Custom_JiebaTokenizer
- name: LanguageModelFeaturizer
model_name: "bert"
model_weights: "bert-base-chinese"
- name: RegexFeaturizer
- name: RegexEntityExtractor
- name: DIETClassifier
epochs: 100
tensorboard_log_directory: ./log
learning_rate: 0.001
- name: ResponseSelector
epochs: 1000
learning_rate: 0.001
- name: FallbackClassifier
threshold: 0.4
ambiguity_threshold: 0.1
- name: EntitySynonymMapper
Test case running results:
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