UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)

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Hello author, thank you for sharing this. I am having some problems with the code you provided and would like to ask you about it. There are two tasks, task A succeeds but task B fails, the error message is like this: UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128) Looking forward to your reply! (Note: Task A has 9995 texts, Task B has more than 36000 texts)

The error is reported as follows:

{
"name": "UnicodeEncodeError",
"message": "'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)",
"stack": "---------------------------------------------------------------------------
UnicodeEncodeError Traceback (most recent call last)
Cell In[22], line 2
1 # 查看主题
----> 2 topics, probs = topic_model.fit_transform(docs, embeddings=embeddings) #传入训练好的词向量
3 topic_model.get_topic_info()

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\bertopic\_bertopic.py:389, in BERTopic.fit_transform(self, documents, embeddings, images, y)
386 umap_embeddings = self._reduce_dimensionality(embeddings, y)
388 # Cluster reduced embeddings
--> 389 documents, probabilities = self._cluster_embeddings(umap_embeddings, documents, y=y)
391 # Sort and Map Topic IDs by their frequency
392 if not self.nr_topics:

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\bertopic\_bertopic.py:3218, in BERTopic._cluster_embeddings(self, umap_embeddings, documents, partial_fit, y)
3216 else:
3217 try:
-> 3218 self.hdbscan_model.fit(umap_embeddings, y=y)
3219 except TypeError:
3220 self.hdbscan_model.fit(umap_embeddings)

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\hdbscan\hdbscan_.py:1205, in HDBSCAN.fit(self, X, y)
1195 kwargs.pop("prediction_data", None)
1196 kwargs.update(self.metric_kwargs)
1198 (
1199 self.labels,
1200 self.probabilities_,
1201 self.cluster_persistence_,
1202 self._condensed_tree,
1203 self._single_linkage_tree,
1204 self._min_spanning_tree,
-> 1205 ) = hdbscan(clean_data, **kwargs)
1207 if self.metric != "precomputed" and not self._all_finite:
1208 # remap indices to align with original data in the case of non-finite entries.
1209 self._condensed_tree = remap_condensed_tree(
1210 self._condensed_tree, internal_to_raw, outliers
1211 )

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\hdbscan\hdbscan_.py:837, in hdbscan(X, min_cluster_size, min_samples, alpha, cluster_selection_epsilon, max_cluster_size, metric, p, leaf_size, algorithm, memory, approx_min_span_tree, gen_min_span_tree, core_dist_n_jobs, cluster_selection_method, allow_single_cluster, match_reference_implementation, **kwargs)
824 (single_linkage_tree, result_min_span_tree) = memory.cache(
825 _hdbscan_prims_kdtree
826 )(
(...)
834 **kwargs
835 )
836 else:
--> 837 (single_linkage_tree, result_min_span_tree) = memory.cache(
838 _hdbscan_boruvka_kdtree
839 )(
840 X,
841 min_samples,
842 alpha,
843 metric,
844 p,
845 leaf_size,
846 approx_min_span_tree,
847 gen_min_span_tree,
848 core_dist_n_jobs,
849 **kwargs
850 )
851 else: # Metric is a valid BallTree metric
852 # TO DO: Need heuristic to decide when to go to boruvka;
853 # still debugging for now
854 if X.shape[1] > 60:

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\memory.py:312, in NotMemorizedFunc.call(self, *args, **kwargs)
311 def call(self, *args, **kwargs):
--> 312 return self.func(*args, **kwargs)

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\hdbscan\hdbscan_.py:340, in _hdbscan_boruvka_kdtree(X, min_samples, alpha, metric, p, leaf_size, approx_min_span_tree, gen_min_span_tree, core_dist_n_jobs, **kwargs)
337 X = X.astype(np.float64)
339 tree = KDTree(X, metric=metric, leaf_size=leaf_size, **kwargs)
--> 340 alg = KDTreeBoruvkaAlgorithm(
341 tree,
342 min_samples,
343 metric=metric,
344 leaf_size=leaf_size // 3,
345 approx_min_span_tree=approx_min_span_tree,
346 n_jobs=core_dist_n_jobs,
347 **kwargs
348 )
349 min_spanning_tree = alg.spanning_tree()
350 # Sort edges of the min_spanning_tree by weight

File hdbscan\_hdbscan_boruvka.pyx:392, in hdbscan._hdbscan_boruvka.KDTreeBoruvkaAlgorithm.init()

File hdbscan\_hdbscan_boruvka.pyx:426, in hdbscan._hdbscan_boruvka.KDTreeBoruvkaAlgorithm._compute_bounds()

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\parallel.py:1909, in Parallel.call(self, iterable)
1906 self._start_time = time.time()
1908 if not self._managed_backend:
-> 1909 n_jobs = self._initialize_backend()
1910 else:
1911 n_jobs = self._effective_n_jobs()

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\parallel.py:1359, in Parallel._initialize_backend(self)
1357 """Build a process or thread pool and return the number of workers"""
1358 try:
-> 1359 n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
1360 **self._backend_args)
1361 if self.timeout is not None and not self._backend.supports_timeout:
1362 warnings.warn(
1363 'The backend class {!r} does not support timeout. '
1364 "You have set 'timeout={}' in Parallel but "
1365 "the 'timeout' parameter will not be used.".format(
1366 self._backend.class.name,
1367 self.timeout))

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\_parallel_backends.py:538, in LokyBackend.configure(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)
534 if n_jobs == 1:
535 raise FallbackToBackend(
536 SequentialBackend(nesting_level=self.nesting_level))
--> 538 self._workers = get_memmapping_executor(
539 n_jobs, timeout=idle_worker_timeout,
540 env=self._prepare_worker_env(n_jobs=n_jobs),
541 context_id=parallel._id, **memmappingexecutor_args)
542 self.parallel = parallel
543 return n_jobs

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\executor.py:20, in get_memmapping_executor(n_jobs, **kwargs)
19 def get_memmapping_executor(n_jobs, **kwargs):
---> 20 return MemmappingExecutor.get_memmapping_executor(n_jobs, **kwargs)

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\executor.py:42, in MemmappingExecutor.get_memmapping_executor(cls, n_jobs, timeout, initializer, initargs, env, temp_folder, context_id, **backend_args)
39 reuse = _executor_args is None or _executor_args == executor_args
40 _executor_args = executor_args
---> 42 manager = TemporaryResourcesManager(temp_folder)
44 # reducers access the temporary folder in which to store temporary
45 # pickles through a call to manager.resolve_temp_folder_name. resolving
46 # the folder name dynamically is useful to use different folders across
47 # calls of a same reusable executor
48 job_reducers, result_reducers = get_memmapping_reducers(
49 unlink_on_gc_collect=True,
50 temp_folder_resolver=manager.resolve_temp_folder_name,
51 **backend_args)

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\_memmapping_reducer.py:540, in TemporaryResourcesManager.init(self, temp_folder_root, context_id)
534 if context_id is None:
535 # It would be safer to not assign a default context id (less silent
536 # bugs), but doing this while maintaining backward compatibility
537 # with the previous, context-unaware version get_memmaping_executor
538 # exposes too many low-level details.
539 context_id = uuid4().hex
--> 540 self.set_current_context(context_id)

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\_memmapping_reducer.py:544, in TemporaryResourcesManager.set_current_context(self, context_id)
542 def set_current_context(self, context_id):
543 self._current_context_id = context_id
--> 544 self.register_new_context(context_id)

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\memmapping_reducer.py:569, in TemporaryResourcesManager.register_new_context(self, context_id)
562 new_folder_name = (
563 "joblib_memmapping_folder{}{}{}".format(
564 os.getpid(), self._id, context_id)
565 )
566 new_folder_path, _ = _get_temp_dir(
567 new_folder_name, self._temp_folder_root
568 )
--> 569 self.register_folder_finalizer(new_folder_path, context_id)
570 self._cached_temp_folders[context_id] = new_folder_path

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\_memmapping_reducer.py:585, in TemporaryResourcesManager.register_folder_finalizer(self, pool_subfolder, context_id)
578 def register_folder_finalizer(self, pool_subfolder, context_id):
579 # Register the garbage collector at program exit in case caller forgets
580 # to call terminate explicitly: note we do not pass any reference to
581 # ensure that this callback won't prevent garbage collection of
582 # parallel instance and related file handler resources such as POSIX
583 # semaphores and pipes
584 pool_module_name = whichmodule(delete_folder, 'delete_folder')
--> 585 resource_tracker.register(pool_subfolder, "folder")
587 def _cleanup():
588 # In some cases the Python runtime seems to set delete_folder to
589 # None just before exiting when accessing the delete_folder
(...)
594 # because joblib should only use relative imports to allow
595 # easy vendoring.
596 delete_folder = import(
597 pool_module_name, fromlist=['delete_folder']
598 ).delete_folder

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py:179, in ResourceTracker.register(self, name, rtype)
177 """Register a named resource, and increment its refcount."""
178 self.ensure_running()
--> 179 self._send("REGISTER", name, rtype)

File d:\zhuhongchang\anaconda\envs\bertopic_evo\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py:196, in ResourceTracker._send(self, cmd, name, rtype)
192 if len(name) > 512:
193 # posix guarantees that writes to a pipe of less than PIPE_BUF
194 # bytes are atomic, and that PIPE_BUF >= 512
195 raise ValueError("name too long")
--> 196 msg = f"{cmd}:{name}:{rtype}
".encode("ascii")
197 nbytes = os.write(self._fd, msg)
198 assert nbytes == len(msg)

UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)"
}

Reproduction

1.task A：
import numpy as np
from bertopic import BERTopic
from transformers.pipelines import pipeline
from sentence_transformers import SentenceTransformer
from umap import UMAP
from hdbscan import HDBSCAN
from sklearn.feature_extraction.text import CountVectorizer


with open(r'D:\zhuhongchang\python_study\萝卜快跑\2.数据预处理\4.分词\微博内容_切词.txt', 'r', encoding='utf-8') as file:
  docs = file.readlines()
print('条数: ', len(docs))
print('预览第一条: ', docs[0])

vectorizer_model = None

# 1. 词向量模型，同时加载本地训练好的词向量
#embedding_model = pipeline("feature-extraction", model="bert-base-chinese") # 使用bert-base-chinese
#embeddings = np.load(R'C:\Users\李书智\Downloads\BBC.npy') # 使用bert-base-chinese向量
#print('向量shape：', embeddings.shape)

# 替换: 使用hfl模型
# embedding_model = pipeline("feature-extraction", model="hfl/chinese-bert-wwm")
# embeddings = np.load('C:\Users\李书智\Downloads\BBC\emb.npy') 
# print('向量shape：', embeddings.shape)

# 替换: 使用Sentencetransformers模型
embedding_model = embedding_model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2',)
embeddings = np.load(r'D:\zhuhongchang\python_study\萝卜快跑\3.数据处理\1.文本转向量\词向量setence-transformer.npy')
print(embeddings.shape)

# 2. 创建UMAP降维模型
umap_model = UMAP(
  n_neighbors=15,
  n_components=5,
  min_dist=0.0,
  metric='cosine',
  random_state=30  # ⚠️ 防止随机 https://maartengr.github.io/BERTopic/faq.html
)

# 3. 创建HDBSCAN聚类模型
# 如果要建设离群值，可以减小下面两个参数min_cluster_size min_samples
# https://hdbscan.readthedocs.io/en/latest/faq.html
hdbscan_model = HDBSCAN(
  min_cluster_size=50,
  min_samples=50,
  metric='euclidean'
)

# 5. 创建CountVectorizer模型
vectorizer_model = CountVectorizer(stop_words=['洛阳', '旅游', '文化'])

# 6. 正式创建BERTopic模型
topic_model = BERTopic(
  embedding_model=embedding_model,
  vectorizer_model=vectorizer_model,
  umap_model=umap_model,
  hdbscan_model=hdbscan_model,
)

# 查看主题
topics, probs = topic_model.fit_transform(docs, embeddings=embeddings) #传入训练好的词向量
topic_model.get_topic_info()

2.task B：
import numpy as np
from bertopic import BERTopic
from transformers.pipelines import pipeline
from sentence_transformers import SentenceTransformer
from umap import UMAP
from hdbscan import HDBSCAN
from sklearn.feature_extraction.text import CountVectorizer

with open(r'C:\Users\李书智\Desktop\切词后.txt', 'r', encoding='utf-8') as file:
  docs = file.readlines()
print('条数: ', len(docs))
print('预览第一条: ', docs[0])

vectorizer_model = None

# 1. 词向量模型，同时加载本地训练好的词向量
#embedding_model = pipeline("feature-extraction", model="bert-base-chinese") # 使用bert-base-chinese
#embeddings = np.load(R'C:\Users\李书智\Downloads\BBC.npy') # 使用bert-base-chinese向量
#print('向量shape：', embeddings.shape)

# 替换: 使用hfl模型
# embedding_model = pipeline("feature-extraction", model="hfl/chinese-bert-wwm")
# embeddings = np.load('C:\Users\李书智\Downloads\BBC\emb.npy') 
# print('向量shape：', embeddings.shape)

# 替换: 使用Sentencetransformers模型
embedding_model = embedding_model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2',)
embeddings = np.load(r'C:\Users\李书智\Desktop\STweiboNEIRONG.npy')
print(embeddings.shape)

# 2. 创建UMAP降维模型
umap_model = UMAP(
  n_neighbors=15,
  n_components=5,
  min_dist=0.0,
  metric='cosine',
  random_state=30  # ⚠️ 防止随机 https://maartengr.github.io/BERTopic/faq.html
)

# 3. 创建HDBSCAN聚类模型
# 如果要建设离群值，可以减小下面两个参数min_cluster_size min_samples
# https://hdbscan.readthedocs.io/en/latest/faq.html
hdbscan_model = HDBSCAN(
  min_cluster_size=50,
  min_samples=50,
  metric='euclidean'
)

# 5. 创建CountVectorizer模型
vectorizer_model = CountVectorizer(stop_words=['洛阳', '旅游', '文化'])

# 6. 正式创建BERTopic模型
topic_model = BERTopic(
  embedding_model=embedding_model,
  vectorizer_model=vectorizer_model,
  umap_model=umap_model,
  hdbscan_model=hdbscan_model,
)

# 查看主题
topics, probs = topic_model.fit_transform(docs, embeddings=embeddings) #传入训练好的词向量
topic_model.get_topic_info()


### BERTopic Version

unknow

Reproduce task B from the provided notebook context and inspect the BERTopic.fit_transform path through _cluster_embeddings. Then compare the traceback's joblib resource_tracker._send failure with the successful task A case; done should identify a reproducible project-side failure and a confirmed regression test or documented dependency boundary.