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BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-summary

Text Classification
BERTopic
English
summcomparer
Model card Files Community
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  1. README.md +92 -0
  2. config.json +15 -0
  3. ctfidf.safetensors +3 -0
  4. ctfidf_config.json +0 -0
  5. topic_embeddings.safetensors +3 -0
  6. topics.json +3200 -0
README.md ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ---
3
+ tags:
4
+ - bertopic
5
+ library_name: bertopic
6
+ pipeline_tag: text-classification
7
+ ---
8
+
9
+ # BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-summary
10
+
11
+ This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
12
+ BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
13
+
14
+ ## Usage
15
+
16
+ To use this model, please install BERTopic:
17
+
18
+ ```
19
+ pip install -U bertopic
20
+ ```
21
+
22
+ You can use the model as follows:
23
+
24
+ ```python
25
+ from bertopic import BERTopic
26
+ topic_model = BERTopic.load("pszemraj/BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-summary")
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+
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+ topic_model.get_topic_info()
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+ ```
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+
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+ ## Topic overview
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+
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+ * Number of topics: 25
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+ * Number of training documents: 1960
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+
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+ <details>
37
+ <summary>Click here for an overview of all topics.</summary>
38
+
39
+ | Topic ID | Topic Keywords | Topic Frequency | Label |
40
+ |----------|----------------|-----------------|-------|
41
+ | -1 | question - it - going - they - she | 11 | -1_question_it_going_they |
42
+ | 0 | merging - merge - operations - concept - computation | 62 | 0_merging_merge_operations_concept |
43
+ | 1 | rainsford - island - sailors - hunted - hunting | 208 | 1_rainsford_island_sailors_hunted |
44
+ | 2 | film - films - noir - dissertation - cinema | 116 | 2_film_films_noir_dissertation |
45
+ | 3 | patients - predicting - predict - prediction - unsupervised | 114 | 3_patients_predicting_predict_prediction |
46
+ | 4 | cogvideo - videos - cogview2 - cog - pretrained | 108 | 4_cogvideo_videos_cogview2_cog |
47
+ | 5 | frozen - sled - snow - princess - hans | 108 | 5_frozen_sled_snow_princess |
48
+ | 6 | dory - coral - fish - gill - ocean | 103 | 6_dory_coral_fish_gill |
49
+ | 7 | captions - encoder - image - images - caption | 103 | 7_captions_encoder_image_images |
50
+ | 8 | law - assignments - lectures - assignment - learning | 99 | 8_law_assignments_lectures_assignment |
51
+ | 9 | convolutional - segmentation - imaging - pathology - superpixels | 98 | 9_convolutional_segmentation_imaging_pathology |
52
+ | 10 | enhancement - enhancing - vocoding - vocoder - audio | 97 | 10_enhancement_enhancing_vocoding_vocoder |
53
+ | 11 | tokenization - medical - health - words - embeddings | 97 | 11_tokenization_medical_health_words |
54
+ | 12 | gillis - scene - script - sunset - movie | 93 | 12_gillis_scene_script_sunset |
55
+ | 13 | anthony - antony - scene - guy - his | 92 | 13_anthony_antony_scene_guy |
56
+ | 14 | topic - projects - sociology - research - students | 90 | 14_topic_projects_sociology_research |
57
+ | 15 | peter - conversation - asks - questions - cheesy | 88 | 15_peter_conversation_asks_questions |
58
+ | 16 | sniper - marine - unarmed - combat - trained | 86 | 16_sniper_marine_unarmed_combat |
59
+ | 17 | communication - apparatus - method - input - embodiment | 68 | 17_communication_apparatus_method_input |
60
+ | 18 | words - phrases - political - unsupervised - topic | 27 | 18_words_phrases_political_unsupervised |
61
+ | 19 | clustering - similarity - unsupervised - topic - plagiarism | 23 | 19_clustering_similarity_unsupervised_topic |
62
+ | 20 | book - novel - father - read - arrives | 21 | 20_book_novel_father_read |
63
+ | 21 | topic - loans - clustering - loan - analyze | 19 | 21_topic_loans_clustering_loan |
64
+ | 22 | sciences - science - society - research - scientists | 16 | 22_sciences_science_society_research |
65
+ | 23 | dynamics - situation - quantum - mechanics - note | 13 | 23_dynamics_situation_quantum_mechanics |
66
+
67
+ </details>
68
+
69
+ ## Training hyperparameters
70
+
71
+ * calculate_probabilities: True
72
+ * language: None
73
+ * low_memory: False
74
+ * min_topic_size: 10
75
+ * n_gram_range: (1, 1)
76
+ * nr_topics: None
77
+ * seed_topic_list: None
78
+ * top_n_words: 10
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+ * verbose: True
80
+
81
+ ## Framework versions
82
+
83
+ * Numpy: 1.22.4
84
+ * HDBSCAN: 0.8.29
85
+ * UMAP: 0.5.3
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+ * Pandas: 1.5.3
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+ * Scikit-Learn: 1.2.2
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+ * Sentence-transformers: 2.2.2
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+ * Transformers: 4.29.2
90
+ * Numba: 0.56.4
91
+ * Plotly: 5.13.1
92
+ * Python: 3.10.11
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+ }
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