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pszemraj
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BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-summary

Text Classification
BERTopic
English
Model card Files Community
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  1. README.md +91 -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 +3151 -0
README.md ADDED
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1
+
2
+ ---
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+ tags:
4
+ - bertopic
5
+ library_name: bertopic
6
+ pipeline_tag: text-classification
7
+ ---
8
+
9
+ # BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-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-sentence-t5-xl-summary")
27
+
28
+ 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: 24
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+ * Number of training documents: 1960
35
+
36
+ <details>
37
+ <summary>Click here for an overview of all topics.</summary>
38
+
39
+ | Topic ID | Topic Keywords | Topic Frequency | Label |
40
+ |----------|----------------|-----------------|-------|
41
+ | -1 | no_saic_raw_sp - sep_4 - sec - data - image | 13 | -1_no_saic_raw_sp_sep_4_sec_data |
42
+ | 0 | lecture - applications - methods - learning - topics | 104 | 0_lecture_applications_methods_learning |
43
+ | 1 | cogvideo - videos - cogview2 - cog - video | 303 | 1_cogvideo_videos_cogview2_cog |
44
+ | 2 | ship - rainsford - hunted - island - hunts | 117 | 2_ship_rainsford_hunted_island |
45
+ | 3 | films - dissertation - film - noir - identity | 106 | 3_films_dissertation_film_noir |
46
+ | 4 | linguistics - language - languages - foundational - systems | 104 | 4_linguistics_language_languages_foundational |
47
+ | 5 | nemo - dory - transcript - clownfish - fish | 103 | 5_nemo_dory_transcript_clownfish |
48
+ | 6 | train - bruno - washington - station - tennis | 102 | 6_train_bruno_washington_station |
49
+ | 7 | images - representations - image - captions - representation | 102 | 7_images_representations_image_captions |
50
+ | 8 | merge - merging - explain - concept - problems | 102 | 8_merge_merging_explain_concept |
51
+ | 9 | enhancement - enhancing - recordings - improve - waveforms | 100 | 9_enhancement_enhancing_recordings_improve |
52
+ | 10 | arendelle - elsa - frozen - kristoff - olaf | 99 | 10_arendelle_elsa_frozen_kristoff |
53
+ | 11 | scene - story - script - movie - gillis | 97 | 11_scene_story_script_movie |
54
+ | 12 | lecture - lemmatization - nlp - medical - techniques | 96 | 12_lecture_lemmatization_nlp_medical |
55
+ | 13 | questions - topics - conversation - terrance - talk | 85 | 13_questions_topics_conversation_terrance |
56
+ | 14 | sniper - kill - fury - combat - narrator | 81 | 14_sniper_kill_fury_combat |
57
+ | 15 | images - lecture - ezurich - pathology - medical | 67 | 15_images_lecture_ezurich_pathology |
58
+ | 16 | timeseries - framework - interpretability - representations - next_concept | 37 | 16_timeseries_framework_interpretability_representations |
59
+ | 17 | prediction - predictions - forecasting - predict - markov | 27 | 17_prediction_predictions_forecasting_predict |
60
+ | 18 | images - imaging - computational - convolutional - lecture | 27 | 18_images_imaging_computational_convolutional |
61
+ | 19 | technology - treatment - methods - medical - detection | 27 | 19_technology_treatment_methods_medical |
62
+ | 20 | novel - translation - henry - read - learn | 23 | 20_novel_translation_henry_read |
63
+ | 21 | abridged - brief - synopsis - short - citations | 22 | 21_abridged_brief_synopsis_short |
64
+ | 22 | lecture - pathology - medical - computational - patients | 16 | 22_lecture_pathology_medical_computational |
65
+
66
+ </details>
67
+
68
+ ## Training hyperparameters
69
+
70
+ * calculate_probabilities: True
71
+ * language: None
72
+ * low_memory: False
73
+ * min_topic_size: 10
74
+ * n_gram_range: (1, 1)
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+ * nr_topics: None
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+ * seed_topic_list: None
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+ * top_n_words: 10
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+ * verbose: True
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+
80
+ ## Framework versions
81
+
82
+ * Numpy: 1.22.4
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+ * HDBSCAN: 0.8.29
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+ * 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
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+ * Numba: 0.56.4
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+ * Plotly: 5.13.1
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+ * Python: 3.10.11
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