Comments (2)
Hi!
Yes, unfortunately our model is of bert-large-uncased
type originally and therefore on a Colab GPU you can only run batch sizes of 8.
If you reduce max_seq_length
you might get bigger batch sizes. Check how much would get truncated if you reduced max_seq_length to something like 70 or 80. If you work with tweets you can sometimes safely reduce it and run bigger batch sizes.
Also check out our Colab using TF 2.x on TPU which allows you to use bigger batches. We've now added a Colab for both GPU and TPU training to the README. Let me know if this works for you.
from covid-twitter-bert.
Thank you smaller batch size worked in my case.
I tried to reduce max_seq_length but it affects my performance. So I will stick to smaller batch size for now. Thanks!
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
- 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
- Loss HOT 2
- 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.