Comments (1)
The dev_f1
uses the validation dataset for evaluation, while f1
uses the test dataset. Lastly, best_f1
indicates the best f1 score evaluated against the test dataset.
Eventually, the model that is written to disk is simply the last checkpoint. Since you should report the f1 of this model, it's best to use f1
.
from ditto.
Related Issues (20)
- Code implementation - Training HOT 9
- ImportError: cannot import name 'LongformerModel' from 'transformers' (transformers=2.8)
- How is your F1 score calculated, whether you use weight or macro or micro HOT 1
- random result when inference 2 similar textual data HOT 3
- Add --save_model flag to the training example
- Error when using --summarize with matcher.py HOT 1
- Whether are special tokens like [COL] [VAL] and attribute names added into the vocabulary?
- [Question] Can I use this package in a notebook environment?
- Inferencing HOT 1
- How (code) to serialize the inputs ? HOT 1
- ModuleNotFoundError: No module named 'click._bashcomplete' HOT 1
- ValueError: not enough values to unpack (expected 2, got 1) - Textual/Company HOT 5
- drop_col gives error? HOT 5
- evaluation method seems to assign a new f1 value as a best score without computing f1 value by best_th=0.5 HOT 1
- Adding custom tokens
- F1 Score for Structured/Beer on paper can't be reproduced
- The link for the Company.zip file seems to be invalid. HOT 1
- Summarization sometimes removes attribute names between [COL] and [VAL]
- training HOT 7
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from ditto.