# Can we use Model trained on Spacy==2.0.18 with Spacy==2.1.3?
**URL:** https://support.prodi.gy/t/can-we-use-model-trained-on-spacy-2-0-18-with-spacy-2-1-3/1402
**Category:** Uncategorized
**Tags:** solved, spacy, off-topic
**Created:** [April 16, 2019, 9:32am UTC](https://support.prodi.gy/t/can-we-use-model-trained-on-spacy-2-0-18-with-spacy-2-1-3/1402 "2019-04-16T09:32:17Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@abhinandansrivastava](https://support.prodi.gy/u/abhinandansrivastava)
#### Post date: [April 16, 2019, 9:32am UTC](https://support.prodi.gy/t/can-we-use-model-trained-on-spacy-2-0-18-with-spacy-2-1-3/1402/1 "2019-04-16T09:32:17Z")
When i tried to load the spacy custom model it gave me a error.
`nlp = spacy.load('/home/cloud/Spacy/Custom_model')`
```
error
```
Traceback (most recent call last)
in
----\> 1 nlp = spacy.load(’/home/cloud/Spacy/Custom\_model’)
```
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/spacy/ __init__.py in load(name, **overrides)
25 if depr_path not in (True, False, None):
26 deprecation_warning(Warnings.W001.format(path=depr_path))
---> 27 return util.load_model(name, **overrides)
28
29
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/spacy/util.py in load_model(name, **overrides)
131 return load_model_from_package(name, **overrides)
132 if Path(name).exists(): # path to model data directory
--> 133 return load_model_from_path(Path(name), **overrides)
134 elif hasattr(name, "exists"): # Path or Path-like to model data
135 return load_model_from_path(name, **overrides)
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/spacy/util.py in load_model_from_path(model_path, meta, **overrides)
171 component = nlp.create_pipe(name, config=config)
172 nlp.add_pipe(component, name=name)
--> 173 return nlp.from_disk(model_path)
174
175
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/spacy/language.py in from_disk(self, path, exclude, disable)
784 # Convert to list here in case exclude is (default) tuple
785 exclude = list(exclude) + ["vocab"]
--> 786 util.from_disk(path, deserializers, exclude)
787 self._path = path
788 return self
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/spacy/util.py in from_disk(path, readers, exclude)
609 # Split to support file names like meta.json
610 if key.split(".")[0] not in exclude:
--> 611 reader(path / key)
612 return path
613
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/site-packages/spacy/language.py in (p)
774 deserializers["meta.json"] = lambda p: self.meta.update(srsly.read_json(p))
775 deserializers["vocab"] = lambda p: self.vocab.from_disk(p) and _fix_pretrained_vectors_name(self)
--> 776 deserializers["tokenizer"] = lambda p: self.tokenizer.from_disk(p, exclude=["vocab"])
777 for name, proc in self.pipeline:
778 if name in exclude:
tokenizer.pyx in spacy.tokenizer.Tokenizer.from_disk()
tokenizer.pyx in spacy.tokenizer.Tokenizer.from_bytes()
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/re.py in compile(pattern, flags)
231 def compile(pattern, flags=0):
232 "Compile a regular expression pattern, returning a pattern object."
--> 233 return _compile(pattern, flags)
234
235 def purge():
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/re.py in _compile(pattern, flags)
299 if not sre_compile.isstring(pattern):
300 raise TypeError("first argument must be string or compiled pattern")
--> 301 p = sre_compile.compile(pattern, flags)
302 if not (flags & DEBUG):
303 if len(_cache) >= _MAXCACHE:
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/sre_compile.py in compile(p, flags)
560 if isstring(p):
561 pattern = p
--> 562 p = sre_parse.parse(p, flags)
563 else:
564 pattern = None
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/sre_parse.py in parse(str, flags, pattern)
853
854 try:
--> 855 p = _parse_sub(source, pattern, flags & SRE_FLAG_VERBOSE, 0)
856 except Verbose:
857 # the VERBOSE flag was switched on inside the pattern. to be
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/sre_parse.py in _parse_sub(source, state, verbose, nested)
414 while True:
415 itemsappend(_parse(source, state, verbose, nested + 1,
--> 416 not nested and not items))
417 if not sourcematch("|"):
418 break
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/sre_parse.py in _parse(source, state, verbose, nested, first)
525 break
526 elif this[0] == "\\":
--> 527 code1 = _class_escape(source, this)
528 else:
529 code1 = LITERAL, _ord(this)
~/miniconda3/envs/tensorflow_gpu/lib/python3.6/sre_parse.py in _class_escape(source, escape)
334 if len(escape) == 2:
335 if c in ASCIILETTERS:
--> 336 raise source.error('bad escape %s' % escape, len(escape))
337 return LITERAL, ord(escape[1])
338 except ValueError:
error: bad escape \p at position 257
```
---
### Author:  [@ines](https://support.prodi.gy/u/ines)
#### Post date: [April 16, 2019, 9:42am UTC](https://support.prodi.gy/t/can-we-use-model-trained-on-spacy-2-0-18-with-spacy-2-1-3/1402/2 "2019-04-16T09:42:24Z")
No – as you can see in the release notes, the new spaCy update requires new models and also training your own models.
You can also find more details on the model versioning [here in the docs](https://spacy.io/models#model-versioning):
> Additionally, the model versioning reflects both the compatibility with spaCy, as well as the major and minor model version. A model version `a.b.c` translates to:
>
> - `a` : **spaCy major version**. For example, `2` for spaCy v2.x.
> - `b` : **Model major version**. Models with a different major version can’t be loaded by the same code. For example, changing the width of the model, adding hidden layers or changing the activation changes the model major version.
> - `c` : **Model minor version**. Same model structure, but different parameter values, e.g. from being trained on different data, for different numbers of iterations, etc.
---
### Author:  [@abhinandansrivastava](https://support.prodi.gy/u/abhinandansrivastava)
#### Post date: [April 16, 2019, 12:31pm UTC](https://support.prodi.gy/t/can-we-use-model-trained-on-spacy-2-0-18-with-spacy-2-1-3/1402/3 "2019-04-16T12:31:23Z")
Hi @ines,
Thanks for the reply.
I am trying to use EntityRuler which has been introduced in new update.
Trying to
```
import spacy
from spacy.pipeline import EntityRuler
nlp = spacy.load('custom_model')
weights_pattern = [
{"LIKE_NUM": True},
{"LOWER": {"IN": ["pounds","green mountain diapers"]}}
]
patterns = [{"label": "DIAPER", "pattern": weights_pattern}]
ruler = EntityRuler(nlp, patterns=patterns)
nlp.add_pipe(ruler, before="ner")
doc = nlp("green mountain diapers have best wipes")
print([(ent.text, ent.label_) for ent in doc.ents])
[]
```
> It is not able to find the green mountain diapers.