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karinegabsschon
/
BERTopic_Technological

Text Classification
BERTopic
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Add BERTopic model

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Files changed (6) hide show
  1. README.md +97 -0
  2. config.json +17 -0
  3. ctfidf.safetensors +3 -0
  4. ctfidf_config.json +0 -0
  5. topic_embeddings.safetensors +3 -0
  6. topics.json +4691 -0
README.md ADDED
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1
+
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+ ---
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+ tags:
4
+ - bertopic
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+ library_name: bertopic
6
+ pipeline_tag: text-classification
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+ ---
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+
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+ # BERTopic_Technological
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+
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+ This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
12
+ BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
13
+
14
+ ## Usage
15
+
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+ To use this model, please install BERTopic:
17
+
18
+ ```
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+ pip install -U bertopic
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+ ```
21
+
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+ You can use the model as follows:
23
+
24
+ ```python
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+ from bertopic import BERTopic
26
+ topic_model = BERTopic.load("karinegabsschon/BERTopic_Technological")
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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: 28
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+ * Number of training documents: 947
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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 | electric - battery - car - new - ev | 12 | -1_electric_battery_car_new |
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+ | 0 | tesla - musk - elon - company - elon musk | 225 | 0_tesla_musk_elon_company |
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+ | 1 | charging - ev - ev charging - solutions - infrastructure | 93 | 1_charging_ev_ev charging_solutions |
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+ | 2 | byd - charging - minutes - new - tesla | 70 | 2_byd_charging_minutes_new |
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+ | 3 | kia - electric - car - ev - cars | 55 | 3_kia_electric_car_ev |
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+ | 4 | charging - car - battery - cars - mobility | 47 | 4_charging_car_battery_cars |
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+ | 5 | xiaomi - su7 - yu7 - china - car | 45 | 5_xiaomi_su7_yu7_china |
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+ | 6 | id - volkswagen - every1 - id every1 - vw | 39 | 6_id_volkswagen_every1_id every1 |
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+ | 7 | leapmotor - ferrari - electric - c10 - stellantis | 30 | 7_leapmotor_ferrari_electric_c10 |
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+ | 8 | cars - adac - vehicles - combustion - breakdown | 26 | 8_cars_adac_vehicles_combustion |
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+ | 9 | electric - charging - renault - vehicles - iberdrola | 23 | 9_electric_charging_renault_vehicles |
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+ | 10 | car - electric - fisker - ev - battery | 22 | 10_car_electric_fisker_ev |
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+ | 11 | foxconn - mitsubishi - taiwan - software - ev | 22 | 11_foxconn_mitsubishi_taiwan_software |
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+ | 12 | slate - truck - pickup - bezos - slate auto | 21 | 12_slate_truck_pickup_bezos |
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+ | 13 | byd - chinese - tesla - electric - cars | 20 | 13_byd_chinese_tesla_electric |
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+ | 14 | mercedes - cla - new - amg - eq | 19 | 14_mercedes_cla_new_amg |
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+ | 15 | charging - octopus - pod - home - drivers | 17 | 15_charging_octopus_pod_home |
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+ | 16 | nissan - micra - toyota - new - car | 17 | 16_nissan_micra_toyota_new |
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+ | 17 | india - ev - tvs - mobility - infineon | 16 | 17_india_ev_tvs_mobility |
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+ | 18 | mg - ev - s5 - mg4 - cyberster | 16 | 18_mg_ev_s5_mg4 |
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+ | 19 | chinese - china - defence - cars - electric | 16 | 19_chinese_china_defence_cars |
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+ | 20 | vinfast - vf - vietnam - vietnamese - electric | 15 | 20_vinfast_vf_vietnam_vietnamese |
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+ | 21 | audi - rs6 - car - new - döllner | 15 | 21_audi_rs6_car_new |
64
+ | 22 | xpeng - ai - p7 - chinese - china | 15 | 22_xpeng_ai_p7_chinese |
65
+ | 23 | dolphin - surf - dolphin surf - byd - euros | 14 | 23_dolphin_surf_dolphin surf_byd |
66
+ | 24 | renault - r5 - car - r4 - electric | 13 | 24_renault_r5_car_r4 |
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+ | 25 | bmw - sound - neue - neue klasse - klasse | 12 | 25_bmw_sound_neue_neue klasse |
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+ | 26 | rivian - software - police - vehicle - electric | 12 | 26_rivian_software_police_vehicle |
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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: False
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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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+ * zeroshot_min_similarity: 0.7
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+ * zeroshot_topic_list: None
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+
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+ ## Framework versions
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+
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+ * Numpy: 2.0.2
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+ * HDBSCAN: 0.8.40
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+ * UMAP: 0.5.8
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+ * Pandas: 2.2.2
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+ * Scikit-Learn: 1.6.1
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+ * Sentence-transformers: 4.1.0
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+ * Transformers: 4.53.0
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+ * Numba: 0.60.0
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+ * Plotly: 5.24.1
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+ * Python: 3.11.13
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+ "embedding_model": "ibm-granite/granite-embedding-125m-english"
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