Update README.md

README.md

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-# gqa
+# Graphcore/lxmert-gqa-uncased
+
+BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabeled texts. It enables easy and fast fine-tuning for different downstream task such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. 
+
+It was trained with two objectives in pretraining : Masked language modeling(MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation.  In addition to MLM, NSP is used for jointly pertaining text-pair representations.
+
+It reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks.
 
-This model is a fine-tuned version of [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) on the [Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.9326
-- Accuracy: 0.5934
 
 ## Model description
 
-LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. It acheives the state-of-the-art results on VQA anad GQA. 
+LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modelling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modelling, and visual-question answering objectives. It achieves the state-of-the-art results on VQA anad GQA. 
 
 Paper link : [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://arxiv.org/pdf/1908.07490.pdf)
 
 ## Intended uses & limitations
 
-More information needed
+
+This model is a fine-tuned version of [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) on the [Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.9326
+- Accuracy: 0.5934
 
 ## Training and evaluation data
 
-[Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset
+- [Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset
 
 ## Training procedure