BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-summary
This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
Hierarchy of topics:
Usage
To use this model, please install BERTopic:
pip install -U -q bertopic safetensors
You can use the model as follows:
from bertopic import BERTopic
topic_model = BERTopic.load("pszemraj/BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-summary")
topic_model.visualize_topics()
# for dataframe:
# topic_model.get_topic_info()
predicting new instances:
topic, embedding = topic_model.transform(text)
print(topic)
Topic overview
- Number of topics: 24
- Number of training documents: 1960
<details> <summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | no_saic_raw_sp - sep_4 - sec - data - image | 13 | -1_no_saic_raw_sp_sep_4_sec_data |
| 0 | lecture - applications - methods - learning - topics | 104 | 0_lecture_applications_methods_learning |
| 1 | cogvideo - videos - cogview2 - cog - video | 303 | 1_cogvideo_videos_cogview2_cog |
| 2 | ship - rainsford - hunted - island - hunts | 117 | 2_ship_rainsford_hunted_island |
| 3 | films - dissertation - film - noir - identity | 106 | 3_films_dissertation_film_noir |
| 4 | linguistics - language - languages - foundational - systems | 104 | 4_linguistics_language_languages_foundational |
| 5 | nemo - dory - transcript - clownfish - fish | 103 | 5_nemo_dory_transcript_clownfish |
| 6 | train - bruno - washington - station - tennis | 102 | 6_train_bruno_washington_station |
| 7 | images - representations - image - captions - representation | 102 | 7_images_representations_image_captions |
| 8 | merge - merging - explain - concept - problems | 102 | 8_merge_merging_explain_concept |
| 9 | enhancement - enhancing - recordings - improve - waveforms | 100 | 9_enhancement_enhancing_recordings_improve |
| 10 | arendelle - elsa - frozen - kristoff - olaf | 99 | 10_arendelle_elsa_frozen_kristoff |
| 11 | scene - story - script - movie - gillis | 97 | 11_scene_story_script_movie |
| 12 | lecture - lemmatization - nlp - medical - techniques | 96 | 12_lecture_lemmatization_nlp_medical |
| 13 | questions - topics - conversation - terrance - talk | 85 | 13_questions_topics_conversation_terrance |
| 14 | sniper - kill - fury - combat - narrator | 81 | 14_sniper_kill_fury_combat |
| 15 | images - lecture - ezurich - pathology - medical | 67 | 15_images_lecture_ezurich_pathology |
| 16 | timeseries - framework - interpretability - representations - next_concept | 37 | 16_timeseries_framework_interpretability_representations |
| 17 | prediction - predictions - forecasting - predict - markov | 27 | 17_prediction_predictions_forecasting_predict |
| 18 | images - imaging - computational - convolutional - lecture | 27 | 18_images_imaging_computational_convolutional |
| 19 | technology - treatment - methods - medical - detection | 27 | 19_technology_treatment_methods_medical |
| 20 | novel - translation - henry - read - learn | 23 | 20_novel_translation_henry_read |
| 21 | abridged - brief - synopsis - short - citations | 22 | 21_abridged_brief_synopsis_short |
| 22 | lecture - pathology - medical - computational - patients | 16 | 22_lecture_pathology_medical_computational |
</details>
Training hyperparameters
- calculate_probabilities: True
- language: None
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: None
- seed_topic_list: None
- top_n_words: 10
- verbose: True
Framework versions
- Numpy: 1.22.4
- HDBSCAN: 0.8.29
- UMAP: 0.5.3
- Pandas: 1.5.3
- Scikit-Learn: 1.2.2
- Sentence-transformers: 2.2.2
- Transformers: 4.29.2
- Numba: 0.56.4
- Plotly: 5.13.1
- Python: 3.10.11
