Add BERTopic model

README.md

@@ -0,0 +1,76 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# MARTINI_enrich_BERTopic_publicannouncement602967921
+
+This is a [BERTopic](https://github.com/MaartenGr/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:
+
+```python
+from bertopic import BERTopic
+topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_publicannouncement602967921")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 7
+* Number of training documents: 838
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | vaccine - nhs - bbc - censorship - agenda | 22 | -1_vaccine_nhs_bbc_censorship | 
+| 0 | constables - allegations - warwickshire - informant - arrested | 416 | 0_constables_allegations_warwickshire_informant | 
+| 1 | vaccinated - vaers - mhra - injections - myocarditis | 160 | 1_vaccinated_vaers_mhra_injections | 
+| 2 | nhs - vaccinated - broadyorkshirelaw - autism - pupils | 96 | 2_nhs_vaccinated_broadyorkshirelaw_autism | 
+| 3 | ukcitizen2021 - amendments - parliament - supranational - monkeypox | 51 | 3_ukcitizen2021_amendments_parliament_supranational | 
+| 4 | bbcisthevirus - marches - antifa - banners - blff | 48 | 4_bbcisthevirus_marches_antifa_banners | 
+| 5 | donotconsent - unite - parliament - unlawful - sworn | 45 | 5_donotconsent_unite_parliament_unlawful |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: True
+* language: None
+* 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
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 1.26.4
+* HDBSCAN: 0.8.40
+* UMAP: 0.5.7
+* Pandas: 2.2.3
+* Scikit-Learn: 1.5.2
+* Sentence-transformers: 3.3.1
+* Transformers: 4.46.3
+* Numba: 0.60.0
+* Plotly: 5.24.1
+* Python: 3.10.12

config.json

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+{
+  "calculate_probabilities": true,
+  "language": null,
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": null,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": false,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null
+}

ctfidf.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e036bdc5e74e17a91e548cf982588d3310f49a122102da6ee0385c809a3099e9
+size 239544

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1daf2a66bc249fa1ae356ee0eb222a9052daa401c37c0d64400bac6ec953425e
+size 28760

topics.json

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+{
+  "topic_representations": {
+    "-1": [
+      [
+        "vaccine",
+        0.5727126598358154
+      ],
+      [
+        "nhs",
+        0.5359825491905212
+      ],
+      [
+        "bbc",
+        0.5051034092903137
+      ],
+      [
+        "censorship",
+        0.45752352476119995
+      ],
+      [
+        "agenda",
+        0.4427945613861084
+      ]
+    ],
+    "0": [
+      [
+        "constables",
+        0.5053543448448181
+      ],
+      [
+        "allegations",
+        0.4885701537132263
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+      [
+        "warwickshire",
+        0.4731890857219696
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+      [
+        "informant",
+        0.457654744386673
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+      [
+        "arrested",
+        0.45143479108810425
+      ]
+    ],
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+      [
+        "vaccinated",
+        0.5755122900009155
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+      [
+        "vaers",
+        0.5728192925453186
+      ],
+      [
+        "mhra",
+        0.5521559715270996
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+      [
+        "injections",
+        0.5056233406066895
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+      [
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+        0.4781067967414856
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+      [
+        "broadyorkshirelaw",
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+      [
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+      [
+        "pupils",
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+    "3": [
+      [
+        "ukcitizen2021",
+        0.5754400491714478
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+      [
+        "amendments",
+        0.5459703803062439
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+      [
+        "parliament",
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+      [
+        "supranational",
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+      [
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+    "4": [
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+        "bbcisthevirus",
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+      [
+        "marches",
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+      [
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+      [
+        "banners",
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+      [
+        "blff",
+        0.5105221271514893
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+    "5": [
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+        "donotconsent",
+        0.5377591252326965
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+      [
+        "unite",
+        0.5178820490837097
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+      [
+        "parliament",
+        0.5090623497962952
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+      [
+        "unlawful",
+        0.4619373679161072
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