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--- |
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tags: |
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- bertopic |
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- summcomparer |
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- document_text |
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library_name: bertopic |
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pipeline_tag: text-classification |
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inference: false |
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license: apache-2.0 |
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datasets: |
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- pszemraj/summcomparer-gauntlet-v0p1 |
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language: |
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- en |
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--- |
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# BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-document_text |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. |
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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("pszemraj/BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-document_text") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 16 |
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* Number of training documents: 630 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | convolutional - images - networks - superpixels - overfitting | 12 | -1_convolutional_images_networks_superpixels | |
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| 0 | bruno - guy - pdf - screentalk - he | 26 | 0_bruno_guy_pdf_screentalk | |
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| 1 | elsa - arendelle - kristoff - frozen - anna | 94 | 1_elsa_arendelle_kristoff_frozen | |
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| 2 | gillis - script - room - ll - artie | 73 | 2_gillis_script_room_ll | |
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| 3 | interpretation - explanation - theory - structure - merge | 72 | 3_interpretation_explanation_theory_structure | |
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| 4 | topics - topic - documents - corpus - document | 63 | 4_topics_topic_documents_corpus | |
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| 5 | nemo - dory - chum - gill - fish | 56 | 5_nemo_dory_chum_gill | |
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| 6 | films - film - identity - trauma - zinnemann | 54 | 6_films_film_identity_trauma | |
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| 7 | computational - data - pathology - medical - informatics | 47 | 7_computational_data_pathology_medical | |
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| 8 | images - captions - representations - embeddings - image | 26 | 8_images_captions_representations_embeddings | |
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| 9 | zaroff - rainsford - hunt - hunting - general | 24 | 9_zaroff_rainsford_hunt_hunting | |
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| 10 | cogvideo - interpolation - videos - coglm - frames | 24 | 10_cogvideo_interpolation_videos_coglm | |
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| 11 | assignment - essays - questions - projects - students | 17 | 11_assignment_essays_questions_projects | |
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| 12 | things - ll - some - lol - explain | 16 | 12_things_ll_some_lol | |
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| 13 | videos - arxiv - visual - preprint - generative | 13 | 13_videos_arxiv_visual_preprint | |
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| 14 | spectrograms - musecoder - melspectrogram - vocoding - spectrogram | 13 | 14_spectrograms_musecoder_melspectrogram_vocoding | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: True |
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* language: None |
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* low_memory: False |
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* min_topic_size: 10 |
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* 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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## Framework versions |
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* 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 |
