Comments (2)
guillaumegenthial
commented on July 17, 2024
1
@mungg and @lanzhuzhu ,
Try using the 840B.300d vectors as well as the BIOES tagging scheme.
You should be able to get good results (in average).
Note that some runs perform better than others and the results I report on the README are averaged over 5 runs.
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lanzhuzhu
commented on July 17, 2024
I met the same problem. I used the embedding "glove.6B.300d.txt" and tested the two versions of your code. One model is "chars_lstm_lstm_crf model ", the other is "sequence_tagging". From my results, the chars_lstm_lstm_crf model performs worse in the same condition. Is this common?
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Related Issues (20)
- InvalidArgumentError HOT 1
- Question: Effect of missing word in pre-trained word embeddings on model performance. HOT 1
- Is the evaluation metric the same as the ones in the papers?
- Does this evaluation script apply to BIO or BIES? HOT 1
- The pred_ids of `<pad>` is always zero
- 0 precision 0 recall for some custom tags HOT 2
- Why the result is better than that in the papers? #87 HOT 1
- batch size is creating confusion [ when we compare with Research Paper ]
- tensorflow.contrib.estimator HOT 6
- For models/lstm_crf/main.py, Line 171
- Support for TF 2.0 HOT 6
- InvalidArgumentError: labels contains negative values
- has no attribute 'stop_if_no_increase_hook' for tensorflow 1.9 HOT 1
- Which version of numpy will work HOT 1
- There are issues when I use my own datasets HOT 1
- Thanks for your amazing work!
- Visualizing embeddings & improving accuracy
- In reported result, what is difference between best and abs. best ? Also mean + std. deviation doesnt matches with the best result reported in the github page ? HOT 1
- same result on F1, Accuracy, precision
- Prediction script for single line statements
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