Spacy plugin broken by change in Spacy 3.0
eahogue opened this issue · comments
Hi,
When trying to use the spacy plugin, I get:
python disambiguate.py /home/ubuntu/src/ewiser/ewiser.semcor_base.pt
Traceback (most recent call last):
File "disambiguate.py", line 326, in <module>
nlp.add_pipe(wsd, last=True)
File "/home/ubuntu/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/language.py", line 749, in add_pipe
raise ValueError(err)
ValueError: [E966] `nlp.add_pipe` now takes the string name of the registered component factory, not a callable component. Expected string, but got Disambiguator(
(model): LinearTaggerModel(
(embedder): BERTEmbedder(
(bert_model): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(28996, 1024, padding_idx=0)
(position_embeddings): Embedding(512, 1024)
(token_type_embeddings): Embedding(2, 1024)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(1): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(2): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(3): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(4): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(5): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(6): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(7): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(8): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(9): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(10): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(11): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(12): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(13): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(14): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(15): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(16): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(17): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(18): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(19): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(20): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(21): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(22): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(23): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(activation): Tanh()
)
)
)
(decoder): LinearDecoder(
(embed_tokens): FakeInput()
(linears): ModuleList(
(0): Linear(in_features=1024, out_features=512, bias=True)
)
(dropout): Dropout(p=0.2, inplace=False)
(logits): Linear(in_features=512, out_features=117664, bias=False)
(structured_logits): StructuredLogits(
(adjacency_pars): ParameterList(
(0): Parameter containing: [torch.LongTensor of size 2x174717]
(1): Parameter containing: [torch.FloatTensor of size 174717]
(2): Parameter containing: [torch.LongTensor of size 2]
)
)
(norm): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
) (name: 'None').
- If you created your component with `nlp.create_pipe('name')`: remove nlp.create_pipe and call `nlp.add_pipe('name')` instead.
- If you passed in a component like `TextCategorizer()`: call `nlp.add_pipe` with the string name instead, e.g. `nlp.add_pipe('textcat')`.
- If you're using a custom component: Add the decorator `@Language.component` (for function components) or `@Language.factory` (for class components / factories) to your custom component and assign it a name, e.g. `@Language.component('your_name')`. You can then run `nlp.add_pipe('your_name')` to add it to the pipeline.
Which is similar to an issue I've recently seen with another system. Spacy seems to have changed how these custom pipelines work.
Any chance of a fix in the near future? I can try to fix it myself but it would be nice to have a solution from the authors. Presumably, if my diagnosis is correct, you will have a lot of people asking about this soon.
Thanks,
Alan
Ah thanks! I'll get to it as soon as I can
Now we support spacy 3 (with an ugly hack, but it should not break compatibility with previous versions)
Still getting the same issue using spacy==3.0.7 and spacy==3.1
Which spacy 3 version is compatible?