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BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-document_text

Text Classification
BERTopic
English
summcomparer
document_text
Model card Files Community
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  1. README.md +84 -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 +1843 -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-all-roberta-large-v1-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.
13
+
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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:
23
+
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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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+
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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: 17
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+ * Number of training documents: 995
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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 | 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 |
43
+ | 1 | frozen - anna - snow - hans - elsa | 241 | 1_frozen_anna_snow_hans |
44
+ | 2 | closeup - shot - viewpoint - umpire - camera | 211 | 2_closeup_shot_viewpoint_umpire |
45
+ | 3 | dory - gill - coral - marlin - ocean | 171 | 3_dory_gill_coral_marlin |
46
+ | 4 | operations - structure - operation - theory - interpretation | 60 | 4_operations_structure_operation_theory |
47
+ | 5 | spatial - identity - movement - identities - noir | 59 | 5_spatial_identity_movement_identities |
48
+ | 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 |
51
+ | 9 | learning - assignment - data - research - project | 22 | 9_learning_assignment_data_research |
52
+ | 10 | cogvideo - videos - videogpt - video - clips | 21 | 10_cogvideo_videos_videogpt_video |
53
+ | 11 | lstm - recurrent - encoder - seq2seq - neural | 18 | 11_lstm_recurrent_encoder_seq2seq |
54
+ | 12 | improve - next - do - going - good | 17 | 12_improve_next_do_going |
55
+ | 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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+
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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
65
+ * low_memory: False
66
+ * 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
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+ * Plotly: 5.13.1
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+ * Python: 3.10.11
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+ "embedding_model": "sentence-transformers/all-roberta-large-v1"
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