update installation instructions, fix typos

README.md

@@ -10,21 +10,17 @@ that an application developer might need for Indic languages.
 ### Installation on Linux
 
 ```bash
-pip install http://download.pytorch.org/whl/cpu/torch-1.0.0-cp36-cp36m-linux_x86_64.whl
+pip install torch==1.3.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
 pip install inltk
 ``` 
 
-### Installation on Windows 10 (experimental)
-```bash
-pip install https://download.pytorch.org/whl/cpu/torch-1.1.0-cp36-cp36m-win_amd64.whl
-pip install inltk
-```
+Note: Just make sure to pick the correct torch wheel url, according to the needed 
+platform and python version, which you will find [here](https://pytorch.org/get-started/locally/#pip-1).
 
 iNLTK runs on CPU, as is the desired behaviour for most
 of the Deep Learning models in production.
 
-The first command above will install pytorch-cpu, which, as the name suggests,
- does not have cuda support.
+The first command above will install pytorch for cpu, which, as the name suggests, does not have cuda support.
 
 `Note: inltk is currently supported only on Linux and Windows 10 with Python >= 3.6`
 
@@ -166,7 +162,7 @@ Example:
 ```
 
 `get_sentence_encoding` returns 400 dimensional encoding of the sentence from
-ULMFiT LM Encoder trained in repositories linked below.
+ULMFiT LM Encoder of `<code-of-language>` trained in repositories linked below.
 
 
 **Get Sentence Similarity**
@@ -257,6 +253,6 @@ and [Maithili](https://github.com/goru001/inltk/issues/10) support
 * iNLTK was [trending on GitHub](https://github.motakasoft.com/trending/ranking/monthly/?d=2019-05-01&l=python&page=2) in May 2019
 * iNLTK has had [17,000+ Downloads](
 https://console.cloud.google.com/bigquery?sq=375816891401:185fda81bdc64eb79b98c6b28c77a62a
-) till Nov, 2019
+) till Nov 2019
 
 

inltk/download_assets.py

@@ -13,8 +13,8 @@
 async def download_file(url, dest, fname):
     if (dest/f'{fname}').exists(): return False
     os.makedirs(dest, exist_ok=True)
-    print('Downloading Model... This might take time, depending on your internet connection! Please have patience!'
-          'Don\'t worry, we\'ll only do this for the first time :)')
+    print('Downloading Model. This might take time, depending on your internet connection. Please be patient.\n'
+          'We\'ll only do this for the first time.')
     async with aiohttp.ClientSession() as session:
         async with session.get(url) as response:
             data = await response.read()

inltk/inltk.py

@@ -4,7 +4,7 @@
 from .download_assets import setup_language, verify_language, check_all_languages_identifying_model
 from inltk.tokenizer import LanguageTokenizer
 from .const import tokenizer_special_cases
-from inltk.utils import cos_sim
+from inltk.utils import cos_sim, reset_models
 
 lcodes = LanguageCodes()
 all_language_codes = lcodes.get_all_language_codes()
@@ -75,9 +75,7 @@ def remove_foreign_languages(input: str, host_language_code: str):
 
 
 def reset_language_identifying_models():
-    path = Path(__file__).parent
-    shutil.rmtree(path / 'models' / 'all')
-    return
+    reset_models('all')
 
 
 def get_embedding_vectors(input: str, language_code: str):

inltk/utils.py

@@ -4,3 +4,9 @@
 # cosine similarity
 def cos_sim(v1, v2):
     return F.cosine_similarity(Tensor(v1).unsqueeze(0), Tensor(v2).unsqueeze(0)).mean().item()
+
+
+def reset_models(folder_name: str):
+    path = Path(__file__).parent
+    shutil.rmtree(path / 'models' / f'{folder_name}')
+    return

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="inltk",
-    version="0.5.0",
+    version="0.6.1",
     author="Gaurav",
     author_email="contactgauravforwork@gmail.com",
     description="Natural Language Toolkit for Indian Languages (iNLTK)",