implement topics over time

(nllp) MacBook-Air-3:lnlp user$ python main.py tests/test_data/data.csv

Welcome to the NLLP CLI!
Loaded data from tests/test_data/data.csv

1. Run a Topic Model
2. Run an Optimization routine for a Topic Model (GPU reccomended)
3. Run a Classification Model
4. Load Global Configuration Files
5. Exit
   Choose an option: 1
   {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}]}
6. Select LLM to generate Embeddings
7. Select Dimensionality Reduction Technique
8. Select Clustering Technique
9. Fine Tuning
10. Plotting
11. Save Session Configuration
12. Run Topic Model
13. Load Session Configuration
14. Back
15. Exit
    Choose an option: 1
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}]}
16. all-MiniLM-L6-v2
17. all-MiniLM-L12-v2
18. multi-qa-MiniLM-L6-cos-v1
19. all-mpnet-base-v2
20. Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit
21. Muennighoff/SGPT-125M-weightedmean-nli-bitfit
22. Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit
23. Add huggingface Model
24. Back
25. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'all-MiniLM-L12-v2'}]}
26. Select LLM to generate Embeddings
27. Select Dimensionality Reduction Technique
28. Select Clustering Technique
29. Fine Tuning
30. Plotting
31. Save Session Configuration
32. Run Topic Model
33. Load Session Configuration
34. Back
35. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'all-MiniLM-L12-v2'}, {'Topic': 'Dimensionality Reduction'}]}
36. UMAP
37. PCA
38. t-SNE
39. Truncated SVD
40. Factor Analysis
41. Back
42. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'all-MiniLM-L12-v2'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}]}
43. Select LLM to generate Embeddings
44. Select Dimensionality Reduction Technique
45. Select Clustering Technique
46. Fine Tuning
47. Plotting
48. Save Session Configuration
49. Run Topic Model
50. Load Session Configuration
51. Back
52. Exit
    Choose an option: 3
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'all-MiniLM-L12-v2'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}]}
53. hdbscan
54. kmeans
55. spectral clustering
56. dbscan
57. agglomerative clustering
58. birch
59. affinity propagation
60. mean shift
61. Back
62. Exit
    Choose an option: 3
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'all-MiniLM-L12-v2'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}]}
63. Select LLM to generate Embeddings
64. Select Dimensionality Reduction Technique
65. Select Clustering Technique
66. Fine Tuning
67. Plotting
68. Save Session Configuration
69. Run Topic Model
70. Load Session Configuration
71. Back
72. Exit
    Choose an option: 7
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'all-MiniLM-L12-v2'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'BERTopic(calculate_probabilities=False, ctfidf_model=ClassTfidfTransformer(...), embedding_model=SentenceTransformer(...), hdbscan_model=HDBSCAN(...), language=None, low_memory=False, min_topic_size=10, n_gram_range=(1, 1), nr_topics=None, representation_model=None, seed_topic_list=None, top_n_words=10, umap_model=PCA(...), vectorizer_model=CountVectorizer(...), verbose=False, zeroshot_min_similarity=0.7, zeroshot_topic_list=None)'}]}
    Number of boolean topics: 0
    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
    []
    No plotting options selected. Visualizing all topics, documents, and terms.
    An error occurred. Please try again.
    Would you like to see the error trace? (y/n): y
    Traceback (most recent call last):
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 46, in run
    self._process_responses(self.landing, self.driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
    self._process_responses(menu.parent, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
    self._process_responses(menu.parent, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
    self._process_responses(menu.parent, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 66, in _process_responses
    driver.run_topic_model()
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 112, in run_topic_model
    self._visualize_topics(model, topics, dir)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 119, in _visualize_topics
    topic_viz = model.visualize_topics()
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 2249, in visualize_topics
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/plotting/_topics.py", line 78, in visualize_topics
    IndexError: arrays used as indices must be of integer (or boolean) type

allow for the comma separated choice sequence to be a dictionary, if it is a dictionary then the value will also be parsed in the sequence execution.

Driver object to store logic related to executing fine tuning

Create an object to store all the related logic

add documentation and scripting to be able to run topic models using the VACC

need a shell script with slurm configurations and a setup.txt file

Need to be able to calculate word shifts for different languages

Search for & find labeled data on words and happiness scores (1-10)
For now just focus on Portuguese

Topic Menu will have a TopicModelDriver
Landing Menu will still have a GlobalDriver

Sometimes users talk about multiple topics in one post/comment. To parse out these topics, splitting larger documents into smaller ones is a possible solution.

Set a word or sentence limit for docs, if longer split into multiple

configure vacc scripts

ability to pass in sequence of choices to driver and have it execute the correct menu choice handling

• zipf distributions and wordshifts and PDS for each year(comp to current year)
• make smoothed timeseries of different values for
• make smoothed timeseries of different values for each year (PDS, Sentiment...)

The clusters created in the Topic Model need to be validated using unsupervised clustering metrics.

Append evaluation metrics to topic size distribution csv

learn more option that opens a browser with more information on a specific feature that a menu implements

(nllp) MacBook-Air-3:lnlp user$ python main.py tests/test_data/data.csv

Welcome to the NLLP CLI!
Loaded data from tests/test_data/data.csv

1. Run a Topic Model
2. Run an Optimization routine for a Topic Model (GPU reccomended)
3. Run a Classification Model
4. Load Global Configuration Files
5. Exit
   Choose an option: 1
   {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}]}
6. Select LLM to generate Embeddings
7. Select Dimensionality Reduction Technique
8. Select Clustering Technique
9. Fine Tuning
10. Plotting
11. Save Session Configuration
12. Run Topic Model
13. Load Session Configuration
14. Back
15. Exit
    Choose an option: 1
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}]}
16. all-MiniLM-L6-v2
17. all-MiniLM-L12-v2
18. multi-qa-MiniLM-L6-cos-v1
19. all-mpnet-base-v2
20. Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit
21. Muennighoff/SGPT-125M-weightedmean-nli-bitfit
22. Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit
23. Add huggingface Model
24. Back
25. Exit
    Choose an option: 6
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}]}
26. Select LLM to generate Embeddings
27. Select Dimensionality Reduction Technique
28. Select Clustering Technique
29. Fine Tuning
30. Plotting
31. Save Session Configuration
32. Run Topic Model
33. Load Session Configuration
34. Back
35. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}]}
36. UMAP
37. PCA
38. t-SNE
39. Truncated SVD
40. Factor Analysis
41. Back
42. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}]}
43. Select LLM to generate Embeddings
44. Select Dimensionality Reduction Technique
45. Select Clustering Technique
46. Fine Tuning
47. Plotting
48. Save Session Configuration
49. Run Topic Model
50. Load Session Configuration
51. Back
52. Exit
    Choose an option: 3
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}]}
53. hdbscan
54. kmeans
55. spectral clustering
56. dbscan
57. agglomerative clustering
58. birch
59. affinity propagation
60. mean shift
61. Back
62. Exit
    Choose an option: 3
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}]}
63. Select LLM to generate Embeddings
64. Select Dimensionality Reduction Technique
65. Select Clustering Technique
66. Fine Tuning
67. Plotting
68. Save Session Configuration
69. Run Topic Model
70. Load Session Configuration
71. Back
72. Exit
    Choose an option: 4
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'Fine Tuning'}]}
73. Enable 2-grams
74. Enable 3-grams
75. Ignore Words
76. Enable BM25 weighting
77. Reduce frequent words
78. Enable KeyBERT algorithm
79. Enable ZeroShotClassification
80. Enable Maximal Marginal Relevance
81. Enable Part of Speech filtering
82. Back
83. Exit
    Choose an option: 1
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 2-grams'}]}
84. Select LLM to generate Embeddings
85. Select Dimensionality Reduction Technique
86. Select Clustering Technique
87. Fine Tuning
88. Plotting
89. Save Session Configuration
90. Run Topic Model
91. Load Session Configuration
92. Back
93. Exit
    Choose an option: 5
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 2-grams'}, {'Topic': 'Plotting'}]}
94. Enable Topic Visualizations
95. Enable Document Visualizations
96. Enable Term Visualizations
97. Enable All Visualizations
98. Specify Plot Directory
99. Specify Web Browser
100. Back
101. Exit
     Choose an option: 4
     {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 2-grams'}, {'Topic': 'Plotting'}, {'Plotting': 'Enable All Visualizations'}]}
102. Select LLM to generate Embeddings
103. Select Dimensionality Reduction Technique
104. Select Clustering Technique
105. Fine Tuning
106. Plotting
107. Save Session Configuration
108. Run Topic Model
109. Load Session Configuration
110. Back
111. Exit
     Choose an option: 5
     {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 2-grams'}, {'Topic': 'Plotting'}, {'Plotting': 'Enable All Visualizations'}, {'Topic': 'Plotting'}]}
112. Enable Topic Visualizations
113. Enable Document Visualizations
114. Enable Term Visualizations
115. Enable All Visualizations
116. Specify Plot Directory
117. Specify Web Browser
118. Back
119. Exit
     Choose an option: 5
     Enter the plot directory: tests
     directory : tests
     {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 2-grams'}, {'Topic': 'Plotting'}, {'Plotting': 'Enable All Visualizations'}, {'Topic': 'Plotting'}, {'Plotting': 'tests'}]}
120. Select LLM to generate Embeddings
121. Select Dimensionality Reduction Technique
122. Select Clustering Technique
123. Fine Tuning
124. Plotting
125. Save Session Configuration
126. Run Topic Model
127. Load Session Configuration
128. Back
129. Exit
     Choose an option: 7
     {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Cluster Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-nli-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Cluster'}, {'Cluster': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 2-grams'}, {'Topic': 'Plotting'}, {'Plotting': 'Enable All Visualizations'}, {'Topic': 'Plotting'}, {'Plotting': 'tests'}, {'Topic': 'BERTopic(calculate_probabilities=False, ctfidf_model=ClassTfidfTransformer(...), embedding_model=SentenceTransformer(...), hdbscan_model=HDBSCAN(...), language=None, low_memory=False, min_topic_size=10, n_gram_range=(1, 1), nr_topics=None, representation_model=None, seed_topic_list=None, top_n_words=10, umap_model=PCA(...), vectorizer_model=CountVectorizer(...), verbose=False, zeroshot_min_similarity=0.7, zeroshot_topic_list=None)'}]}
     Number of boolean topics: 0
     [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
     [{'Plotting': 'Enable All Visualizations'}, {'Plotting': 'tests'}]
     An error occurred. Please try again.
     Would you like to see the error trace? (y/n): y
     Traceback (most recent call last):
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 46, in run
     self._process_responses(self.landing, self.driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
     self._process_responses(response, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
     self._process_responses(response, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
     self._process_responses(menu.parent, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
     self._process_responses(response, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
     self._process_responses(menu.parent, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
     self._process_responses(response, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
     self._process_responses(menu.parent, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
     self._process_responses(response, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
     self._process_responses(menu.parent, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
     self._process_responses(response, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
     self._process_responses(menu.parent, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
     self._process_responses(response, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 68, in _process_responses
     self._process_responses(menu.parent, driver)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 66, in _process_responses
     driver.run_topic_model()
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 106, in run_topic_model
     self._visualize_topics(model, topics, dir)
     File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 119, in visualize_topics
     topic_viz = model.visualize_topics()
     File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/bertopic.py", line 2249, in visualize_topics
     File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/plotting/topics.py", line 79, in visualize_topics
     File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 2887, in fit_transform
     self.fit(X, y, force_all_finite)
     File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 2780, in fit
     self.embedding, aux_data = self.fit_embed_data(
     File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 2826, in fit_embed_data
     return simplicial_set_embedding(
     File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 1086, in simplicial_set_embedding
     graph.data[graph.data < (graph.data.max() / float(n_epochs_max))] = 0.0
     File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/numpy/core/_methods.py", line 41, in _amax
     return umr_maximum(a, axis, None, out, keepdims, initial, where)
     ValueError: zero-size array to reduction operation maximum which has no identity

(nllp) MacBook-Air-3:lnlp user$ python main.py tests/test_data/data.csv

Welcome to the NLLP CLI!
Loaded data from tests/test_data/data.csv

1. Run a Topic Model
2. Run an Optimization routine for a Topic Model (GPU reccomended)
3. Run a Classification Model
4. Load Global Configuration Files
5. Exit
   Choose an option: 1
6. Select LLM to generate Embeddings
7. Select Dimensionality Reduction Technique
8. Select Clustering Technique
9. Fine Tuning
10. Plotting
11. Save Topic Model Configuration
12. Load Topic Model Configuration
13. Save Session Data
14. Run Topic Model
15. Back
16. Exit
    Choose an option: 8
    Please enter the path of the directory to save this sessions data: tests
17. Select LLM to generate Embeddings
18. Select Dimensionality Reduction Technique
19. Select Clustering Technique
20. Fine Tuning
21. Plotting
22. Save Topic Model Configuration
23. Load Topic Model Configuration
24. Save Session Data
25. Run Topic Model
26. Back
27. Exit
    Choose an option: 9
    2024-03-25 15:44:48,448 - BERTopic - Embedding - Transforming documents to embeddings.
    Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [00:43<00:00, 1.45it/s]
    2024-03-25 15:45:31,909 - BERTopic - Embedding - Completed ✓
    2024-03-25 15:45:31,909 - BERTopic - Dimensionality - Fitting the dimensionality reduction algorithm
    UMAP( verbose=True)
    Mon Mar 25 15:45:31 2024 Construct fuzzy simplicial set
    Mon Mar 25 15:45:33 2024 Finding Nearest Neighbors
    Mon Mar 25 15:45:34 2024 Finished Nearest Neighbor Search
    Mon Mar 25 15:45:34 2024 Construct embedding
    Epochs completed: 0%| 0/500 [00:00]completed 0 / 500 epochs
    Epochs completed: 0%| ▎ 1/500 [00:00]completed 50 / 500 epochs
    Epochs completed: 19%| █████████████████████████████████▋ 94/500 [00:00]completed 100 / 500 epochs
    completed 150 / 500 epochs
    Epochs completed: 37%| ██████████████████████████████████████████████████████████████████▏ 186/500 [00:00]completed 200 / 500 epochs
    completed 250 / 500 epochs
    Epochs completed: 55%| ██████████████████████████████████████████████████████████████████████████████████████████████████▎ 276/500 [00:01]completed 300 / 500 epochs
    completed 350 / 500 epochs
    Epochs completed: 73%| ██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ 367/500 [00:01]completed 400 / 500 epochs
    completed 450 / 500 epochs
    Epochs completed: 100%| ██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ 500/500 [00:01]
    Mon Mar 25 15:45:36 2024 Finished embedding
    2024-03-25 15:45:36,318 - BERTopic - Dimensionality - Completed ✓
    2024-03-25 15:45:36,318 - BERTopic - Cluster - Start clustering the reduced embeddings
    2024-03-25 15:45:36,343 - BERTopic - Cluster - Completed ✓
    2024-03-25 15:45:36,346 - BERTopic - Representation - Extracting topics from clusters using representation models.
    2024-03-25 15:45:36,454 - BERTopic - Representation - Completed ✓
    No plotting options selected. Visualizing all topics, documents, and terms.
    /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/spectral.py:521: RuntimeWarning: k >= N for N * N square matrix. Attempting to use scipy.linalg.eigh instead.
    eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
    Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k.
    An error occurred. Please try again.
    Would you like to see the error trace? (y/n): y
    Traceback (most recent call last):
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 162, in visualize_topics
    topic_viz = model.visualize_topics()
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/bertopic.py", line 2249, in visualize_topics
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/plotting/topics.py", line 79, in visualize_topics
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 2887, in fit_transform
    self.fit(X, y, force_all_finite)
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 2780, in fit
    self.embedding, aux_data = self.fit_embed_data(
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 2826, in fit_embed_data
    return simplicial_set_embedding(
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/umap.py", line 1106, in simplicial_set_embedding
    embedding = spectral_layout(
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/spectral.py", line 304, in spectral_layout
    return _spectral_layout(
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/umap_learn-0.5.5-py3.9.egg/umap/spectral.py", line 521, in _spectral_layout
    eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py", line 1608, in eigsh
    raise TypeError("Cannot use scipy.linalg.eigh for sparse A with "
    TypeError: Cannot use scipy.linalg.eigh for sparse A with k >= N. Use scipy.linalg.eigh(A.toarray()) or reduce k.

Build out the fine tuning menus for the CLI

From landing :
2. Fine Tune a LLM
->

1. Upload Model Weights -> enter filepath
2. Upload Data -> enter filepath
3. Specify System prompt -> enter system prompt
4. Specify Training and Validation -> Menu
   -> 1. Learning Rate
   2. Num Epochs
   3. Output Dir
   4. ?
5. Commence Fine Tuning

Traceback (most recent call last):
File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 46, in run
self._process_responses(self.landing, self.driver)
File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 64, in _process_responses
self._process_responses(response, driver)
File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 67, in _process_responses
driver.run_topic_model(from_file=True)
File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 72, in run_topic_model
topics, _ = model.fit_transform(self.session.data)
File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 433, in fit_transform
File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 3778, in _extract_topics
File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 3977, in _c_tf_idf
File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/feature_extraction/text.py", line 1340, in fit
self.fit_transform(raw_documents)
File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/base.py", line 1467, in wrapper
estimator._validate_params()
File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/base.py", line 666, in _validate_params
validate_parameter_constraints(
File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/utils/_param_validation.py", line 95, in validate_parameter_constraints
raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'ngram_range' parameter of CountVectorizer must be an instance of 'tuple'. Got [1, 2] instead.

Factory Object to build Fine Tune Objects

built on skopt pipeline

python main.py tests/test_data/data.csv

Welcome to the NLLP CLI!
Loaded data from tests/test_data/data.csv

1. Run a Topic Model
2. Run an Optimization routine for a Topic Model (GPU reccomended)
3. Run a Classification Model
4. Load Global Configuration Files
5. Exit
   Choose an option: 1
   {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}]}
6. Select LLM to generate Embeddings
7. Select Dimensionality Reduction Technique
8. Select Clustering Technique
9. Fine Tuning
10. Plotting
11. Save Session Configuration
12. Run Topic Model
13. Load Session Configuration
14. Back
15. Exit
    Choose an option: 1
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}]}
16. all-MiniLM-L6-v2
17. all-MiniLM-L12-v2
18. multi-qa-MiniLM-L6-cos-v1
19. all-mpnet-base-v2
20. Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit
21. Muennighoff/SGPT-125M-weightedmean-nli-bitfit
22. Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit
23. Add huggingface Model
24. Back
25. Exit
    Choose an option: 5
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}]}
26. Select LLM to generate Embeddings
27. Select Dimensionality Reduction Technique
28. Select Clustering Technique
29. Fine Tuning
30. Plotting
31. Save Session Configuration
32. Run Topic Model
33. Load Session Configuration
34. Back
35. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}]}
36. UMAP
37. PCA
38. t-SNE
39. Truncated SVD
40. Factor Analysis
41. Back
42. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}]}
43. Select LLM to generate Embeddings
44. Select Dimensionality Reduction Technique
45. Select Clustering Technique
46. Fine Tuning
47. Plotting
48. Save Session Configuration
49. Run Topic Model
50. Load Session Configuration
51. Back
52. Exit
    Choose an option: 3
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}]}
53. hdbscan
54. kmeans
55. spectral clustering
56. dbscan
57. agglomerative clustering
58. birch
59. affinity propagation
60. mean shift
61. Back
62. Exit
    Choose an option: 3
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}]}
63. Select LLM to generate Embeddings
64. Select Dimensionality Reduction Technique
65. Select Clustering Technique
66. Fine Tuning
67. Plotting
68. Save Session Configuration
69. Run Topic Model
70. Load Session Configuration
71. Back
72. Exit
    Choose an option: 4
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}, {'Topic': 'Fine Tuning'}]}
73. Enable 2-grams
74. Enable 3-grams
75. Ignore Words
76. Enable BM25 weighting
77. Reduce frequent words
78. Enable KeyBERT algorithm
79. Enable ZeroShotClassification
80. Enable Maximal Marginal Relevance
81. Enable Part of Speech filtering
82. Back
83. Exit
    Choose an option: 2
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 3-grams'}]}
84. Select LLM to generate Embeddings
85. Select Dimensionality Reduction Technique
86. Select Clustering Technique
87. Fine Tuning
88. Plotting
89. Save Session Configuration
90. Run Topic Model
91. Load Session Configuration
92. Back
93. Exit
    Choose an option: 7
    Building topic model from logs
    [{'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}]
    [{'Dimensionality Reduction': 'PCA'}]
    [{'Clustering': 'spectral clustering'}]
    {}
    {}
    {'errors': [], 'info': ['Initialized Global Session Object and Global Driver', 'Initialized Landing Menu', 'Initialized Embeddings Menu', 'Initialized Dimensionality Reduction Menu', 'Initialized Clustering Menu', 'Initialized Fine Tuning Menu', 'Initialized Plotting Menu', 'Initialized ConfigMenu Menu', 'Initialized Topic Menu', 'Initialized ConfigMenu Menu'], 'data': [{'Landing': 'Topic'}, {'Topic': 'Embeddings'}, {'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}, {'Topic': 'Dimensionality Reduction'}, {'Dimensionality Reduction': 'PCA'}, {'Topic': 'Clustering'}, {'Clustering': 'spectral clustering'}, {'Topic': 'Fine Tuning'}, {'Fine Tuning': 'Enable 3-grams'}, {'Topic': 'BERTopic(calculate_probabilities=False, ctfidf_model=ClassTfidfTransformer(...), embedding_model=SentenceTransformer(...), hdbscan_model=SpectralClustering(...), language=None, low_memory=False, min_topic_size=10, n_gram_range=(1, 1), nr_topics=None, representation_model=None, seed_topic_list=None, top_n_words=10, umap_model=PCA(...), vectorizer_model=CountVectorizer(...), verbose=False, zeroshot_min_similarity=0.7, zeroshot_topic_list=None)'}]}
    Building topic model from logs
    [{'Embeddings': 'Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfit'}]
    [{'Dimensionality Reduction': 'PCA'}]
    [{'Clustering': 'spectral clustering'}]
    {}
    {}
    SentenceTransformer(
    (0): Transformer({'max_seq_length': 300, 'do_lower_case': False}) with Transformer model: GPTNeoModel
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    )
    PCA()
    SpectralClustering()
    /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/numpy/core/_methods.py:176: RuntimeWarning: overflow encountered in multiply
    x = um.multiply(x, x, out=x)
    /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/utils/extmath.py:208: RuntimeWarning: overflow encountered in matmul
    ret = a @ b
    /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/metrics/pairwise.py:383: RuntimeWarning: invalid value encountered in add
    distances += XX
    /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scikit_learn-1.4.1.post1-py3.9-macosx-11.1-arm64.egg/sklearn/cluster/_kmeans.py:704: RuntimeWarning: overflow encountered in square
    lloyd_iter(
    /Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/numpy/core/_methods.py:49: RuntimeWarning: overflow encountered in reduce
    return umr_sum(a, axis, dtype, out, keepdims, initial, where)
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    -2.20754370e-01 5.46759248e-01 -5.12399197e-01 5.31018198e-01]]
    No plotting options selected. Visualizing all topics, documents, and terms.
    An error occurred. Please try again.
    Would you like to see the error trace? (y/n): y
    Traceback (most recent call last):
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 47, in run
    self._process_responses(self.landing, self.driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses
    self._process_responses(menu.parent, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses
    self._process_responses(menu.parent, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses
    self._process_responses(menu.parent, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 65, in _process_responses
    self._process_responses(response, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 72, in _process_responses
    self._process_responses(menu.parent, driver)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/_lnlpcli.py", line 70, in _process_responses
    driver.run_topic_model()
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 128, in run_topic_model
    self._visualize_topics(model, dir)
    File "/Users/user/Desktop/Spring_2024/Research/lnlp/src/drivers/_driver.py", line 135, in _visualize_topics
    hierarchical_topics = model.hierarchical_topics(docs=self.session.data)
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 980, in hierarchical_topics
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/bertopic-0.16.0-py3.9.egg/bertopic/_bertopic.py", line 972, in
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scipy/cluster/hierarchy.py", line 1033, in linkage
    n = int(distance.num_obs_y(y))
    File "/Users/user/anaconda3/envs/nllp/lib/python3.9/site-packages/scipy/spatial/distance.py", line 2657, in num_obs_y
    raise ValueError("The number of observations cannot be determined on "
    ValueError: The number of observations cannot be determined on an empty distance matrix.