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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-summary |
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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-all-roberta-large-v1-summary") |
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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: 25 |
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* Number of training documents: 1960 |
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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 | question - it - going - they - she | 11 | -1_question_it_going_they | |
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| 0 | merging - merge - operations - concept - computation | 62 | 0_merging_merge_operations_concept | |
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| 1 | rainsford - island - sailors - hunted - hunting | 208 | 1_rainsford_island_sailors_hunted | |
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| 2 | film - films - noir - dissertation - cinema | 116 | 2_film_films_noir_dissertation | |
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| 3 | patients - predicting - predict - prediction - unsupervised | 114 | 3_patients_predicting_predict_prediction | |
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| 4 | cogvideo - videos - cogview2 - cog - pretrained | 108 | 4_cogvideo_videos_cogview2_cog | |
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| 5 | frozen - sled - snow - princess - hans | 108 | 5_frozen_sled_snow_princess | |
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| 6 | dory - coral - fish - gill - ocean | 103 | 6_dory_coral_fish_gill | |
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| 7 | captions - encoder - image - images - caption | 103 | 7_captions_encoder_image_images | |
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| 8 | law - assignments - lectures - assignment - learning | 99 | 8_law_assignments_lectures_assignment | |
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| 9 | convolutional - segmentation - imaging - pathology - superpixels | 98 | 9_convolutional_segmentation_imaging_pathology | |
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| 10 | enhancement - enhancing - vocoding - vocoder - audio | 97 | 10_enhancement_enhancing_vocoding_vocoder | |
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| 11 | tokenization - medical - health - words - embeddings | 97 | 11_tokenization_medical_health_words | |
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| 12 | gillis - scene - script - sunset - movie | 93 | 12_gillis_scene_script_sunset | |
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| 13 | anthony - antony - scene - guy - his | 92 | 13_anthony_antony_scene_guy | |
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| 14 | topic - projects - sociology - research - students | 90 | 14_topic_projects_sociology_research | |
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| 15 | peter - conversation - asks - questions - cheesy | 88 | 15_peter_conversation_asks_questions | |
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| 16 | sniper - marine - unarmed - combat - trained | 86 | 16_sniper_marine_unarmed_combat | |
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| 17 | communication - apparatus - method - input - embodiment | 68 | 17_communication_apparatus_method_input | |
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| 18 | words - phrases - political - unsupervised - topic | 27 | 18_words_phrases_political_unsupervised | |
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| 19 | clustering - similarity - unsupervised - topic - plagiarism | 23 | 19_clustering_similarity_unsupervised_topic | |
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| 20 | book - novel - father - read - arrives | 21 | 20_book_novel_father_read | |
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| 21 | topic - loans - clustering - loan - analyze | 19 | 21_topic_loans_clustering_loan | |
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| 22 | sciences - science - society - research - scientists | 16 | 22_sciences_science_society_research | |
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| 23 | dynamics - situation - quantum - mechanics - note | 13 | 23_dynamics_situation_quantum_mechanics | |
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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 |
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