Update README.md

README.md

@@ -18,38 +18,3 @@ The general architecture and experimental results of PhoBERT can be found in our
 
 For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)!
 
-### Installation <a name="install2"></a>
- -  Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
- -  Install `transformers`:
-        - `git clone https://github.com/huggingface/transformers.git`
-        - `cd transformers`
-        - `pip3 install --upgrade .`
-
-### Pre-trained models <a name="models2"></a>
-
-Model | #params | Arch.  | Pre-training data
----|---|---|---
-`vinai/phobert-base` | 135M | base | 20GB  of texts
-`vinai/phobert-large` | 370M | large | 20GB  of texts
-
-### Example usage <a name="usage2"></a>
-
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-phobert = AutoModel.from_pretrained("vinai/phobert-base")
-tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
-
-# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
-line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
-
-input_ids = torch.tensor([tokenizer.encode(line)])
-
-with torch.no_grad():
-    features = phobert(input_ids)  # Models outputs are now tuples
-
-## With TensorFlow 2.0+:
-# from transformers import TFAutoModel
-# phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
-```