Update README.md

README.md

@@ -1,31 +1,47 @@
 ---
 language:
 - en
-license:
-- mit
+license: apache-2.0
 widget:
-- text: "The nodes of a computer network may include [MASK]."
+- text: The nodes of a computer network may include [MASK].
+library_name: transformers
 ---
 
 # NetBERT 📶
 
-**A BERT-base model pre-trained on a huge corpus of computer networking text (~23Gb)**.
+<img align="left" src="illustration.jpg" width="100"/> 
+
+NetBERT is a [BERT-base](https://huggingface.co/bert-base-cased) model further pre-trained on a huge corpus of computer networking text (~23Gb).
 
 ## Usage
-You can use NetBERT with [🤗 transformers](https://github.com/huggingface/transformers):
+
+You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search.
+
+You can use this model directly with a pipeline for [masked language modeling](https://huggingface.co/tasks/fill-mask):
 
 ```python
-import torch
-from transformers import BertTokenizer, BertForMaskedLM
+from transformers import pipeline
+
+unmasker = pipeline('fill-mask', model='antoinelouis/netbert')
+unmasker("The nodes of a computer network may include [MASK].")
+```
+
+You can also use this model to [extract the features](https://huggingface.co/tasks/feature-extraction) of a given text:
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained('antoinelouis/netbert')
+model = AutoModel.from_pretrained('antoinelouis/netbert')
 
-# Load pretrained model and tokenizer
-model = BertForMaskedLM.from_pretrained("antoiloui/netbert")
-tokenizer = BertTokenizer.from_pretrained("antoiloui/netbert")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
 ```
 
 ## Documentation
 
-Detailed documentation on the pre-trained model, its implementation, and the data can be found [here](https://github.com/antoiloui/netbert/blob/master/docs/index.md).
+Detailed documentation on the pre-trained model, its implementation, and the data can be found on [Github](https://github.com/antoiloui/netbert/blob/master/docs/index.md).
 
 ## Citation