Added the malayalam classify

After cloning it to hugginglearners updated the model card and added the pipelinetag for widget

README.md

@@ -1,21 +1,46 @@
 ---
 tags:
 - fastai
+pipeline_tag: 
+- text-classification	
 ---
 
-# Amazing!
+# Malayalam (മലയാളം) Classifier using fastai (Working in Progress)
 
-🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
+🥳 This model is my attempt to use machine learning using Malayalam Language. Huge inspiration from [Malayalam Text Classifier](https://kurianbenoy.com/2022-05-30-malayalamtext-0/). Courtesy to @waydegilliam for [blurr](https://ohmeow.github.io/blurr/text-examples-multilabel.html)
 
-# Some next steps
-1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
+🌈 മലയാളത്തിൽ മെഷീൻ ലീർണിങ് പഠിക്കാനും പിന്നേ പരിചയപ്പെടാനും, to be continued...
 
-2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
+# How its built ? & How to use ? 
 
-3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
+Please find the [notebook](https://nbviewer.org/github/rajeshradhakrishnanmvk/kitchen2.0/blob/feature101-frontend/ml/fastai_X_Hugging_Face_Group_2022.ipynb) used for training the model
 
-Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
+Usage:
 
+First, install the utilities to load the model as well as `blurr`, which was used to train this model.
+
+```bash
+!pip install huggingface_hub[fastai]
+!git clone https://github.com/ohmeow/blurr.git && cd blurr && pip install -e ".[dev]"
+```
+
+```python
+from huggingface_hub import from_pretrained_fastai
+learner = from_pretrained_fastai("rajeshradhakrishnan/ml-news-classify-fastai")
+
+
+sentences = ["ഓഹരി വിപണി തകരുമ്പോള്‍ നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം",
+             "വാര്‍ണറുടെ ഒറ്റക്കയ്യന്‍ ക്യാച്ചില്‍ അമ്പരന്ന് ക്രിക്കറ്റ് ലോകം"]
+
+probs = learner.predict(sentences)
+# 'business', 'entertainment', 'sports', 'technology'
+for idx in range(len(sentences)):
+  print(f"Probability that sentence '{sentences[idx]}' is business is: {100*probs[idx]['probs'][0]:.2f}%")
+  print(f"Probability that sentence '{sentences[idx]}' is entertainment is: {100*probs[idx]['probs'][1]:.2f}%")
+  print(f"Probability that sentence '{sentences[idx]}' is sports is: {100*probs[idx]['probs'][2]:.2f}%")
+  print(f"Probability that sentence '{sentences[idx]}' is technology is: {100*probs[idx]['probs'][3]:.2f}%")
+
+```
 
 ---
 
@@ -23,10 +48,23 @@ Greetings fellow fastlearner 🤝! Don't forget to delete this content from your
 # Model card
 
 ## Model description
-More information needed
+The is a Malayalam classifier model for labels 'business', 'entertainment', 'sports', 'technology'.
 
 ## Intended uses & limitations
-More information needed
+The model can be used to categorize malayalam new sfeed.
 
 ## Training and evaluation data
-More information needed
+
+Data is from the [AI4Bharat-IndicNLP Dataset](https://github.com/AI4Bharat/indicnlp_corpus#indicnlp-news-article-classification-dataset) and wrapper to extract only Malayalam data( [HF dataset](https://huggingface.co/datasets/rajeshradhakrishnan/malayalam_news))!.
+
+## Citation
+
+
+```
+@article{kunchukuttan2020indicnlpcorpus,
+    title={AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages},
+    author={Anoop Kunchukuttan and Divyanshu Kakwani and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
+    year={2020},
+    journal={arXiv preprint arXiv:2005.00085},
+}
+```