Add BERTopic model
Browse files- README.md +79 -0
- config.json +15 -0
- topic_embeddings.safetensors +3 -0
- topics.json +2103 -0
README.md
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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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# cnn_dailymail_123_3000_1500_test
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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("KingKazma/cnn_dailymail_123_3000_1500_test")
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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: 12
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* Number of training documents: 1500
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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 | league - season - klopp - game - club | 13 | -1_league_season_klopp_game |
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| 0 | said - one - year - also - people | 73 | 0_said_one_year_also |
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| 1 | liverpool - player - club - sterling - league | 1088 | 1_liverpool_player_club_sterling |
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| 2 | league - goal - madrid - barcelona - champions | 91 | 2_league_goal_madrid_barcelona |
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| 3 | manchester - united - city - van - gaal | 51 | 3_manchester_united_city_van |
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| 4 | world - first - woods - hamilton - win | 38 | 4_world_first_woods_hamilton |
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| 5 | england - cricket - test - captain - pietersen | 35 | 5_england_cricket_test_captain |
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| 6 | celtic - game - inverness - rangers - player | 31 | 6_celtic_game_inverness_rangers |
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| 7 | mayweather - fight - pacquiao - vegas - las | 28 | 7_mayweather_fight_pacquiao_vegas |
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| 8 | mccoy - national - lady - ride - race | 23 | 8_mccoy_national_lady_ride |
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| 9 | clermont - saracens - cup - england - northampton | 16 | 9_clermont_saracens_cup_england |
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| 10 | chelsea - mourinho - stoke - league - game | 13 | 10_chelsea_mourinho_stoke_league |
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</details>
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## Training hyperparameters
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* calculate_probabilities: True
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* language: english
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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: False
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## Framework versions
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* Numpy: 1.22.4
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* HDBSCAN: 0.8.33
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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: 4.31.0
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* Numba: 0.56.4
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* Plotly: 5.13.1
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* Python: 3.10.6
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config.json
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{
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"calculate_probabilities": true,
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"language": "english",
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"low_memory": false,
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"min_topic_size": 10,
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"n_gram_range": [
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1,
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],
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"nr_topics": null,
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"seed_topic_list": null,
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"top_n_words": 10,
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"verbose": false,
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"embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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}
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topic_embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9011747f412755a9fe6337ae5892ffc8ebad31aa45299a9fb08253f4c4c4796b
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size 18520
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topics.json
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