Comments (2)
Hi Isminoula! Sorry for the late answer - We are using the original BERT implementation in which the loss is the sum of the masked language modelling task and the next sentence prediction task, so MLM + NSP. If you're curious, this is where the magic happens: https://github.com/tensorflow/models/blob/93490036e00f37ecbe6693b9ff4ae488bb8e9270/official/nlp/bert/bert_models.py#L78
Hope this helps!
from covid-twitter-bert.
Thank you @mar-muel for the detailed explanation and for open-sourcing this model.
Closing the ticket, thanks again!
from covid-twitter-bert.
Related Issues (18)
- Add link to preprint in readme HOT 1
- Failed to convert the TensorFlow model to a PyTorch model HOT 4
- Masked worked prediction returning "unused" tokens HOT 3
- CUDA out of memory Error from loading CT-Bert directly from Huggingface HOT 2
- License? HOT 2
- Failed to download dataset HOT 1
- Strategies for downstream NER tasks with more label
- Make one over CORD-19 HOT 1
- the model architecture of COVID-Twitter-BERT v2 MNLI
- No proper encodings for covid-related terms HOT 1
- Failed to find data adapter HOT 2
- A few observations. HOT 2
- TypeError HOT 1
- Logic behind [UNK] token for pronouns HOT 2
- COVID Category (CC) dataset has 4 invalid formats for Tweet ID HOT 1
- What are the tokens for Usernames and Hastags in the BERT vocabulary? HOT 1
- Download Twitter-Sentiment SemEval HOT 3
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from covid-twitter-bert.