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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-all-roberta-large-v1-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 safetensors |
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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-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: 17 |
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* Number of training documents: 995 |
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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 | clustering - convolutional - neural - hierarchical - autoregressive | 11 | -1_clustering_convolutional_neural_hierarchical | |
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| 0 | betty - door - her - gillis - room | 15 | 0_betty_door_her_gillis | |
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| 1 | frozen - anna - snow - hans - elsa | 241 | 1_frozen_anna_snow_hans | |
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| 2 | closeup - shot - viewpoint - umpire - camera | 211 | 2_closeup_shot_viewpoint_umpire | |
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| 3 | dory - gill - coral - marlin - ocean | 171 | 3_dory_gill_coral_marlin | |
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| 4 | operations - structure - operation - theory - interpretation | 60 | 4_operations_structure_operation_theory | |
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| 5 | spatial - identity - movement - identities - noir | 59 | 5_spatial_identity_movement_identities | |
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| 6 | vocabulary - words - topic - text - topics | 45 | 6_vocabulary_words_topic_text | |
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| 7 | encoder - captions - embeddings - decoder - caption | 40 | 7_encoder_captions_embeddings_decoder | |
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| 8 | saw - hounds - smiled - had - hunt | 26 | 8_saw_hounds_smiled_had | |
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| 9 | learning - assignment - data - research - project | 22 | 9_learning_assignment_data_research | |
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| 10 | cogvideo - videos - videogpt - video - clips | 21 | 10_cogvideo_videos_videogpt_video | |
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| 11 | lstm - recurrent - encoder - seq2seq - neural | 18 | 11_lstm_recurrent_encoder_seq2seq | |
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| 12 | improve - next - do - going - good | 17 | 12_improve_next_do_going | |
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| 13 | vocoding - spectrogram - enhancement - melspectrogram - audio | 14 | 13_vocoding_spectrogram_enhancement_melspectrogram | |
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| 14 | probabilities - tagging - probability - words - gram | 12 | 14_probabilities_tagging_probability_words | |
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| 15 | convolutional - segmentation - superpixel - convolutions - superpixels | 12 | 15_convolutional_segmentation_superpixel_convolutions | |
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</details> |
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### hierarchy |
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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 |
