diff --git a/cv/detection/centernet/pytorch/README.md b/cv/detection/centernet/pytorch/README.md
index f02cd94be3dd824f0d01c51fbc9a76d11fe03b07..581cdb58d5053ebce523238fbba4a38837b0c62f 100644
--- a/cv/detection/centernet/pytorch/README.md
+++ b/cv/detection/centernet/pytorch/README.md
@@ -1,6 +1,7 @@
 # CenterNet
 
 ## Model description
+
 Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.
 
 ## Step 1: Installing packages
@@ -9,7 +10,7 @@ Detection identifies objects as axis-aligned boxes in an image. Most successful
 pip3 install -r requirements.txt
 # Compile deformable convolutional(DCNv2)
 cd ./src/lib/models/networks/
-git clone -b pytorch_1.9 https://github.com/lbin/DCNv2.git
+git clone -b pytorch_1.11 https://github.com/lbin/DCNv2.git
 cd ./DCNv2/
 python3 setup.py build develop
 
@@ -18,6 +19,7 @@ python3 setup.py build develop
 ## Step 2: Preparing datasets
 
 ### Go back to the "pytorch/" directory
+
 ```bash
 cd ../../../../../
 ```
@@ -65,21 +67,25 @@ mv resnet18-5c106cde.pth /root/.cache/torch/hub/checkpoints/
 ## Step 3: Training
 
 ### Setup CUDA_VISIBLE_DEVICES variable
+
 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
 ```
 
 ### On single GPU
+
 ```bash
 cd ./cv/detection/centernet/pytorch/src
 python3 main.py ctdet --batch_size 32 --master_batch 15 --lr 1.25e-4  --gpus 0
 ```
 
 ### Multiple GPUs on one machine
+
 ```bash
 cd ./cv/detection/centernet/pytorch/src
 bash run.sh
 ```
 
 ## Reference
+
 https://github.com/bubbliiiing/centernet-pytorch
diff --git a/nlp/language_model/bart_fairseq/pytorch/README.md b/nlp/language_model/bart_fairseq/pytorch/README.md
index 4086ab59a192780263c9d0cef1b70094a043b91d..e87f30d52fae45e62adae58b52805be6d1e642ab 100644
--- a/nlp/language_model/bart_fairseq/pytorch/README.md
+++ b/nlp/language_model/bart_fairseq/pytorch/README.md
@@ -1,15 +1,17 @@
 # BART
 
 ## Model description
+
 BART is sequence-to-sequence model trained with denoising as pretraining
 objective. We show that this pretraining objective is more generic and
-show that we can match RoBERTa results on SQuAD and GLUE and gain 
-state-of-the-art results on summarization (XSum, CNN dataset), 
+show that we can match RoBERTa results on SQuAD and GLUE and gain
+state-of-the-art results on summarization (XSum, CNN dataset),
 long form generative question answering (ELI5) and dialog response
-genration (ConvAI2). 
+genration (ConvAI2).
 
 ## Step 1: Installation
-Bart model is using Fairseq toolbox. Before you run this model, 
+
+Bart model is using Fairseq toolbox. Before you run this model,
 you need to setup Fairseq first.
 
 ```bash
@@ -39,20 +41,22 @@ tar -xzvf bart.large.tar.gz
 ```
 
 ## Step 3: Training
+
 ```bash
 # Finetune on CLUE RTE task
 bash bart.sh
 
 # Inference on GLUE RTE task
-`
 python3 bart.py
 ```
 
 ## Results
 
-| GPUs | QPS | Train Epochs | Accuracy  |
-|------|-----|--------------|------|
-| BI-v100 x8  | 113.18 | 10           | 83.8 |
+
+| GPUs       | QPS    | Train Epochs | Accuracy |
+| ------------ | -------- | -------------- | ---------- |
+| BI-v100 x8 | 113.18 | 10           | 83.8     |
 
 ## Reference
+
 - [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2)
diff --git a/nlp/language_model/bert/pytorch/requirements.txt b/nlp/language_model/bert/pytorch/requirements.txt
index 2f2a216211f3e2a6d31c805d80751329045b9ba8..e15742c210b2f1a2f3818fa475f8a3fc18e5f28c 100644
--- a/nlp/language_model/bert/pytorch/requirements.txt
+++ b/nlp/language_model/bert/pytorch/requirements.txt
@@ -1,6 +1,6 @@
 # progress bars in model download and training scripts
 boto3==1.14.0
-h5py==2.10.0
+h5py==3.10.0
 html2text==2020.1.16
 ipdb==0.13.2
 nltk==3.5