kyubyong / bert_ner Goto Github PK

Data download link invalid.
thank you！！！

In bert paper, it seems that the words start with '##' should not be predicted. And you did compute is_head variable, but why this variable is not used when computing loss ?

The data download link is invalid. can you upload another connection? tank you very much

hi,
Thank you for sharing the code. But I can not find the file 'pytorch_pretrained_bert.py', hence I could not run the code.
Please tell how can I find the file 'pytorch_pretrained_bert.py'. Thanks

My requirement here is given a sentence(sequence), I would like to just extract the entities present in the sequence without classifying them to a type in the NER task. I see that BertForTokenClassification for NER does the classification. Can this be adapted for just the extraction?

Can you give me an idea of how to do entity extraction/identification using BERT?

We know that when loading pretrained bert, max_seqence=512, but when you are dealing with the sentences from data set, each sentence is padding based on the longest sentence of the batch, but I don't see you do truncate when the sequence longer than 512 lengths?

Thanks

Hi,
I was wondering how you are actually calculating your scores.

y_true = np.array([hp.tag2idx[line.split()[1]] for line in open(f, 'r').read().splitlines() if len(line) > 0])
y_pred = np.array([hp.tag2idx[line.split()[2]] for line in open(f, 'r').read().splitlines() if len(line) > 0])

num_proposed = len(y_pred[y_pred>1])
num_correct = (np.logical_and(y_true==y_pred, y_true>1)).astype(np.int).sum()
num_gold = len(y_true[y_true>1])

precision = num_correct / num_proposed
recall = num_correct / num_gold

Can you explain what the above code means?
How does this translate to say recall = TP / TP + FN? Don't you have to use some multi-class method?

Also, why are you only taking the index where y_true>1? Is it because you do not want the Other tag to skew your results? Thanks!

Can I use other datasets on this model?

I'm getting the following error when i try to run the finetuning example:

RuntimeError: CUDA out of memory. Tried to allocate 85.00 MiB (GPU 0; 4.00 GiB total capacity; 3.04 GiB already allocated; 9.21 MiB free; 15.31 MiB cached)

Reducing the batch_size didn't help.