Remove Vectors.from_glove (#5209)

spacy/vectors.pyx

@@ -355,44 +355,6 @@ cdef class Vectors:
                     for i in range(len(queries)) ], dtype="uint64")
         return (keys, best_rows, scores)
 
-    def from_glove(self, path):
-        """Load GloVe vectors from a directory. Assumes binary format,
-        that the vocab is in a vocab.txt, and that vectors are named
-        vectors.{size}.[fd].bin, e.g. vectors.128.f.bin for 128d float32
-        vectors, vectors.300.d.bin for 300d float64 (double) vectors, etc.
-        By default GloVe outputs 64-bit vectors.
-
-        path (unicode / Path): The path to load the GloVe vectors from.
-        RETURNS: A `StringStore` object, holding the key-to-string mapping.
-
-        DOCS: https://spacy.io/api/vectors#from_glove
-        """
-        path = util.ensure_path(path)
-        width = None
-        for name in path.iterdir():
-            if name.parts[-1].startswith("vectors"):
-                _, dims, dtype, _2 = name.parts[-1].split('.')
-                width = int(dims)
-                break
-        else:
-            raise IOError(Errors.E061.format(filename=path))
-        bin_loc = path / "vectors.{dims}.{dtype}.bin".format(dims=dims, dtype=dtype)
-        xp = get_array_module(self.data)
-        self.data = None
-        with bin_loc.open("rb") as file_:
-            self.data = xp.fromfile(file_, dtype=dtype)
-            if dtype != "float32":
-                self.data = xp.ascontiguousarray(self.data, dtype="float32")
-        if self.data.ndim == 1:
-            self.data = self.data.reshape((self.data.size//width, width))
-        n = 0
-        strings = StringStore()
-        with (path / "vocab.txt").open("r") as file_:
-            for i, line in enumerate(file_):
-                key = strings.add(line.strip())
-                self.add(key, row=i)
-        return strings
-
     def to_disk(self, path, **kwargs):
         """Save the current state to a directory.
 

website/docs/api/vectors.md

@@ -326,25 +326,6 @@ performed in chunks, to avoid consuming too much memory. You can set the
 | `sort`       | bool      | Whether to sort the entries returned by score. Defaults to `True`. |
 | **RETURNS**  | tuple     | The most similar entries as a `(keys, best_rows, scores)` tuple.   |
 
-## Vectors.from_glove {#from_glove tag="method"}
-
-Load [GloVe](https://nlp.stanford.edu/projects/glove/) vectors from a directory.
-Assumes binary format, that the vocab is in a `vocab.txt`, and that vectors are
-named `vectors.{size}.[fd.bin]`, e.g. `vectors.128.f.bin` for 128d float32
-vectors, `vectors.300.d.bin` for 300d float64 (double) vectors, etc. By default
-GloVe outputs 64-bit vectors.
-
-> #### Example
->
-> ```python
-> vectors = Vectors()
-> vectors.from_glove("/path/to/glove_vectors")
-> ```
-
-| Name   | Type             | Description                              |
-| ------ | ---------------- | ---------------------------------------- |
-| `path` | unicode / `Path` | The path to load the GloVe vectors from. |
-
 ## Vectors.to_disk {#to_disk tag="method"}
 
 Save the current state to a directory.

website/docs/usage/vectors-similarity.md

@@ -177,37 +177,6 @@ for word, vector in vector_data.items():
     vocab.set_vector(word, vector)
 ```
 
-### Loading GloVe vectors {#custom-loading-glove new="2"}
-
-spaCy comes with built-in support for loading
-[GloVe](https://nlp.stanford.edu/projects/glove/) vectors from a directory. The
-[`Vectors.from_glove`](/api/vectors#from_glove) method assumes a binary format,
-the vocab provided in a `vocab.txt`, and the naming scheme of
-`vectors.{size}.[fd`.bin]. For example:
-
-```yaml
-### Directory structure
-└── vectors
-    ├── vectors.128.f.bin  # vectors file
-    └── vocab.txt          # vocabulary
-```
-
-| File name           | Dimensions | Data type        |
-| ------------------- | ---------- | ---------------- |
-| `vectors.128.f.bin` | 128        | float32          |
-| `vectors.300.d.bin` | 300        | float64 (double) |
-
-```python
-nlp = spacy.load("en_core_web_sm")
-nlp.vocab.vectors.from_glove("/path/to/vectors")
-```
-
-If your instance of `Language` already contains vectors, they will be
-overwritten. To create your own GloVe vectors model package like spaCy's
-[`en_vectors_web_lg`](/models/en-starters#en_vectors_web_lg), you can call
-[`nlp.to_disk`](/api/language#to_disk), and then package the model using the
-[`package`](/api/cli#package) command.
-
 ### Using custom similarity methods {#custom-similarity}
 
 By default, [`Token.vector`](/api/token#vector) returns the vector for its