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Using multiple models in NER #1334
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That's not an error, though. It should work just fine with that warning
…On Sun, Jan 21, 2024, 11:41 PM linlinloo ***@***.***> wrote:
I want to run the following code, but an error occurred.
import stanza
pipe = stanza.Pipeline("en", processors="tokenize,ner", package={"ner":
["ncbi_disease", "ontonotes"]})
doc = pipe("John Bauer works at Stanford and has hip arthritis. He works
for Chris Manning")
print(doc.ents)
WARNING: Language en package default expects mwt, which has been added
I have downloaded ncbi_disease.pt and placed it in
site-packages\stanza\stanza_resources\en\ner What's the problem?and why?
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However, the operation did not yield any results, and a series of errors would appear: ConnectTimeout, MaxRetryError......
|
If it's giving a timeout error, I would guess the most likely culprit is
it's trying to download missing resources and isn't able to connect. You
can add download_method=None to the Pipeline to stop it from downloading
…On Mon, Jan 22, 2024 at 12:13 AM linlinloo ***@***.***> wrote:
However, the operation did not yield any results, and a series of errors
would appear: ConnectTimeout, MaxRetryError......
When I run other code, there is no ncbi_disease in ner. Is it the wrong
package I have put?
Loading these models for language: en (English): | Processor | Package | |
tokenize | combined |
| mwt | combined |
| pos | combined_charlm |
| lemma | combined_nocharlm |
| constituency | ptb3-revised_charlm |
| depparse | combined_charlm |
| sentiment | sstplus |
| ner | ontonotes-ww-multi_charlm |
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Also, I should note that for version 1.7.0, the default NER model is now
"ontonotes-ww-multi_charlm"
there's also
"ontonotes_charlm"
They are named this way so that you can get "nocharlm" models if you want
faster processing. If there's some stale documentation, please let me know
and I'll update it.
|
I find ontonotes_charlm.pt, and I can download it, do you meant that I should replace ontonotes-ww-multi_charlm? |
You can do whatever you like, of course. The ww-multi model was trained on both OntoNotes and the dataset described in this paper
Yes, exactly. I suggest that because it's the most likely reason you're getting timeouts. If the problem is somewhere else, please include the complete stack trace. |
I want to run the following code, but an error occurred.
import stanza
pipe = stanza.Pipeline("en", processors="tokenize,ner", package={"ner": ["ncbi_disease", "ontonotes"]})
doc = pipe("John Bauer works at Stanford and has hip arthritis. He works for Chris Manning")
print(doc.ents)
WARNING: Language en package default expects mwt, which has been added
I have downloaded ncbi_disease.pt and placed it in site-packages\stanza\stanza_resources\en\ner What's the problem?and why?
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