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BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-document_text

Text Classification
BERTopic
English
summcomparer
document_text
Model card Files Community
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Add BERTopic model

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Files changed (6) hide show
  1. README.md +83 -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 +1429 -0
README.md ADDED
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+
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+ ---
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+ tags:
4
+ - bertopic
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+ library_name: bertopic
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+ pipeline_tag: text-classification
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+ ---
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+
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+ # BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-document_text
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+
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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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+
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+ ## Usage
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+
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+ To use this model, please install BERTopic:
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+
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+ ```
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+ pip install -U bertopic
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+ ```
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+
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+ You can use the model as follows:
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+
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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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+
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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: 16
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+ * Number of training documents: 630
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+
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+ <details>
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+ <summary>Click here for an overview of all topics.</summary>
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+
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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 |
42
+ | 0 | bruno - guy - pdf - screentalk - he | 26 | 0_bruno_guy_pdf_screentalk |
43
+ | 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 |
51
+ | 9 | zaroff - rainsford - hunt - hunting - general | 24 | 9_zaroff_rainsford_hunt_hunting |
52
+ | 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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+
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+ </details>
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+
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+ ## Training hyperparameters
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+
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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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+
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+ ## Framework versions
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+
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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
82
+ * Plotly: 5.13.1
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+ * Python: 3.10.11
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