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cnn_dailymail_6789_3000_1500_test
KingKazma/cnn_dailymail_6789_3000_1500_test
cnn_dailymail_6789_3000_1500_test is a text classification model from KingKazma. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Jul 31, 2023
- Updated
- Jul 31, 2023
- SHA
- 456d8167
Try a prompt
Model card
From the Hugging Face model README
cnn_dailymail_6789_3000_1500_test
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.
Usage
To use this model, please install BERTopic:
pip install -U bertopic
You can use the model as follows:
from bertopic import BERTopic
topic_model = BERTopic.load("KingKazma/cnn_dailymail_6789_3000_1500_test")
topic_model.get_topic_info()
Topic overview
- Number of topics: 15
- Number of training documents: 1500
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | season - league - liverpool - player - club | 12 | -1_season_league_liverpool_player |
| 0 | said - one - police - year - people | 151 | 0_said_one_police_year |
| 1 | madrid - league - champions - real - barcelona | 1070 | 1_madrid_league_champions_real |
| 2 | chelsea - united - manchester - van - league | 55 | 2_chelsea_united_manchester_van |
| 3 | fight - pacquiao - ticket - mayweather - boxing | 43 | 3_fight_pacquiao_ticket_mayweather |
| 4 | race - hamilton - rosberg - marathon - vettel | 28 | 4_race_hamilton_rosberg_marathon |
| 5 | england - cook - pietersen - cricket - test | 25 | 5_england_cook_pietersen_cricket |
| 6 | villa - sherwood - benteke - aston - game | 19 | 6_villa_sherwood_benteke_aston |
| 7 | try - minute - huddersfield - bristol - league | 17 | 7_try_minute_huddersfield_bristol |
| 8 | celtic - scottish - rangers - game - inverness | 15 | 8_celtic_scottish_rangers_game |
| 9 | mcilroy - masters - woods - augusta - golf | 14 | 9_mcilroy_masters_woods_augusta |
| 10 | arsenal - wenger - arsenals - reading - coquelin | 14 | 10_arsenal_wenger_arsenals_reading |
| 11 | newcastle - sunderland - advocaat - game - rangers | 13 | 11_newcastle_sunderland_advocaat_game |
| 12 | cup - toulon - saracens - clermont - bath | 12 | 12_cup_toulon_saracens_clermont |
| 13 | stadium - stand - fan - fa - final | 12 | 13_stadium_stand_fan_fa |
Training hyperparameters
- calculate_probabilities: True
- language: english
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: None
- seed_topic_list: None
- top_n_words: 10
- verbose: False
Framework versions
- Numpy: 1.22.4
- HDBSCAN: 0.8.33
- UMAP: 0.5.3
- Pandas: 1.5.3
- Scikit-Learn: 1.2.2
- Sentence-transformers: 2.2.2
- Transformers: 4.31.0
- Numba: 0.56.4
- Plotly: 5.13.1
- Python: 3.10.6
- Task
- Text Classification
- Library
- bertopic
- Created
- Jul 31, 2023
- Updated
- Jul 31, 2023