🚀 Feature request

Fast Tokenizer for DeBERTA-V3 and mDeBERTa-V3

Motivation

DeBERTa V3 is an improved version of DeBERTa. With the V3 version, the authors also released a multilingual model "mDeBERTa-base" that outperforms XLM-R-base. However, DeBERTa V3 currently lacks a FastTokenizer implementation which makes it impossible to use with some of the example scripts (They require a Fa

ENV

Python 3.9
jina 2.5.0

Describe the bug

If i try to dump an image blob to a io.bytesio object an error is thrown

from jina import Document
import io
d =  Document(uri='steam_data/image_store/8c/5b/8c5b265b9c533636.png')
output = io.BytesIO() 

(
    d
    .load_uri_to_image_blob()
    .dump_ima

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.

Rather than simply caching nltk_data until the cache expires and it's forced to re-download the entire nltk_data, we should perform a check on the index.xml which refreshes the cache if it differs from some previous cache.

I would advise doing this in the same way that it's done for requirements.txt:
https://github.com/nltk/nltk/blob/59aa3fb88c04d6151f2409b31dcfe0f332b0c9ca/.github/wor