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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+labeled_data.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -8,7 +8,6 @@ tags:
   - political-text
   - tweets
   - distilbert
-  - active-learning
 datasets:
   - thomasrenault/us_tweet_speech_congress
 metrics:
@@ -19,8 +18,7 @@ pipeline_tag: text-classification
 
 # thomasrenault/topic
 
-A multi-label political topic classifier fine-tuned on US political tweets and congressional speeches.  
-Built on `distilbert-base-uncased` using an **active learning** pipeline with GPT-4o-mini annotation.
+A multi-label political topic classifier fine-tuned on US tweets, campaign speeches and congressional speeches.  Built on `distilbert-base-uncased` with GPT-4o-mini annotation via the OpenAI Batch API.
 
 ## Labels
 
@@ -48,16 +46,12 @@ Documents that match none of the above are implicitly classified as `other topic
 | Problem type | `multi_label_classification` |
 | Training data | ~200,000 labeled documents |
 | Annotation | GPT-4o-mini (temperature=0) via OpenAI Batch API |
-| Strategy | Active learning (uncertainty sampling) |
-| Seed size | 1,000 documents (random) |
-| AL query size | 25,000 documents / round |
-| Epochs (seed) | 4 |
-| Epochs (AL) | 2 (warm-start) |
+| Epochs | 4 |
 | Learning rate | 2e-5 |
 | Batch size | 16 |
 | Max length | 512 tokens |
 | Classification threshold | 0.5 |
-| Domain | US political tweets and congressional floor speeches |
+| Domain | US tweets about policy, campaign speeches and congressional floor speeches |
 
 ## Usage
 
@@ -98,7 +92,6 @@ print(predict("Tax cuts for the wealthy only increase inequality in America."))
 
 - Trained on **US English political text** — may not generalise to other political systems or languages
 - Annotation by GPT-4o-mini introduces model-specific biases in topic boundaries
-- Early training checkpoint (round 0, ~1,600 documents) — performance will improve as active learning progresses
 - Topics reflect the specific research agenda of the parent project; other salient topics (healthcare, climate, etc.) are out of scope
 
 ## Citation

labeled_data.csv

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