We lifted 9 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| digitalepidemiologylab/covid-twitter-bert | canonical | 5 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| configure_optimizer | Ran | digitalepidemiologylab/covid-twitter-bert/run_finetune.py code served (permissive licence) · get_code("07f5f6ae24787306") |
| create_example | Ran | digitalepidemiologylab/covid-twitter-bert/preprocess/create_predict_data.py code served (permissive licence) · get_code("8f68df3de2a27988") |
| get_input_meta_data | Ran | digitalepidemiologylab/covid-twitter-bert/run_finetune.py code served (permissive licence) · get_code("eca754708092452c") |
| get_run_name | Ran | digitalepidemiologylab/covid-twitter-bert/preprocess/create_finetune_data.py code served (permissive licence) · get_code("ae3d0ae784a6cf90") |
| steps_to_run | Ran | digitalepidemiologylab/covid-twitter-bert/utils/model_training_utils.py code served (permissive licence) · get_code("c509d695494f63fb") |
| load_tf2_weights_in_bert | Not yet run | digitalepidemiologylab/covid-twitter-bert/convert_tf2_to_pytorch/convert_tf2_to_pytorch.py code served (permissive licence) · get_code("7d766a22bba3decf") |
| load_tf2_weights_in_bert | Not yet run | digitalepidemiologylab/covid-twitter-bert/convert_tf2_to_pytorch/convert_tf2_to_pytorch_classifier.py code served (permissive licence) · get_code("5b614f10d3d68359") |
| load_tf2_weights_in_bert | Not yet run | digitalepidemiologylab/covid-twitter-bert/convert_tf2_to_pytorch/convert_tf2_to_pytorch_pretrain.py code served (permissive licence) · get_code("2d712ee70005b181") |
| read_data | Not yet run | digitalepidemiologylab/covid-twitter-bert/preprocess/create_finetune_data.py code served (permissive licence) · get_code("3194daec65dc5d7e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2005.07503")
get_code_for_paper("2005.07503")
have("2005.07503")
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