sneakykilli
commited on
Commit
•
39220e8
1
Parent(s):
e411c5e
Add BERTopic model
Browse files- README.md +79 -0
- config.json +17 -0
- ctfidf.safetensors +3 -0
- ctfidf_config.json +0 -0
- topic_embeddings.safetensors +3 -0
- topics.json +665 -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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# Singapore_BERTopic
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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("sneakykilli/Singapore_BERTopic")
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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: 10
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* Number of training documents: 160
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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 | airline - airlines - flights - refund - flight | 6 | -1_airline_airlines_flights_refund |
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| 0 | airline - airlines - flights - singapore - meals | 31 | 0_airline_airlines_flights_singapore |
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| 1 | refund - airline - airlines - complaint - singapore | 43 | 1_refund_airline_airlines_complaint |
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| 2 | baggage - luggage - airlines - airline - bags | 20 | 2_baggage_luggage_airlines_airline |
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| 3 | airlines - passengers - seats - flight - cabin | 14 | 3_airlines_passengers_seats_flight |
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| 4 | refund - repayment - sia - customer - complaints | 11 | 4_refund_repayment_sia_customer |
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| 5 | airlines - airline - fees - singapore - flights | 10 | 5_airlines_airline_fees_singapore |
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| 6 | refund - airline - cancellation - booking - cancel | 9 | 6_refund_airline_cancellation_booking |
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| 7 | miles - airlines - airline - mileage - loyalty | 9 | 7_miles_airlines_airline_mileage |
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| 8 | airline - flight - reviews - booking - customer | 7 | 8_airline_flight_reviews_booking |
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</details>
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## Training hyperparameters
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* calculate_probabilities: False
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* language: None
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* low_memory: False
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* min_topic_size: 5
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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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* zeroshot_min_similarity: 0.7
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* zeroshot_topic_list: None
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## Framework versions
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* Numpy: 1.24.3
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* HDBSCAN: 0.8.33
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* UMAP: 0.5.5
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* Pandas: 2.0.3
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* Scikit-Learn: 1.2.2
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* Sentence-transformers: 2.3.1
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* Transformers: 4.36.2
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* Numba: 0.57.1
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* Plotly: 5.16.1
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* Python: 3.10.12
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config.json
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{
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"calculate_probabilities": false,
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"language": null,
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"low_memory": false,
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"min_topic_size": 5,
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"n_gram_range": [
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1,
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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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"zeroshot_min_similarity": 0.7,
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"zeroshot_topic_list": null,
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"embedding_model": "all-MiniLM-L6-v2"
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}
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ctfidf.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ab6e192a6b8c4a5895c844acd2e5059ef123e06f8aee394e0c21aa10e9d79c0c
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size 77752
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ctfidf_config.json
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The diff for this file is too large to render.
See raw diff
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topic_embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2dcc05adfdd3c56d5d8a07c2f46f5a79ee7e42f8a85d74317e06ab055080d3fa
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size 15448
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topics.json
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{
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"topic_representations": {
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"-1": [
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[
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"airline",
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0.41720014810562134
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],
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[
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"airlines",
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0.41072994470596313
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],
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[
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"flights",
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0.3909800052642822
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],
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[
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"refund",
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+
0.3571051359176636
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],
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[
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"flight",
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+
0.3357126712799072
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],
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[
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"fare",
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26 |
+
0.3030855059623718
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],
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28 |
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[
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"singapore",
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30 |
+
0.29068851470947266
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],
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32 |
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[
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"complaint",
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34 |
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0.274583637714386
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],
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[
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"flying",
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38 |
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0.26368528604507446
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],
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[
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"voucher",
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0.2579178214073181
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]
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],
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"0": [
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[
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"airline",
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0.47997474670410156
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],
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[
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"airlines",
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0.4713612198829651
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],
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[
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"flights",
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0.4244295060634613
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],
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58 |
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[
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"singapore",
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0.36062896251678467
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],
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[
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"meals",
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0.3596476912498474
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],
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[
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"flight",
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0.35365304350852966
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],
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[
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