Update README.md

README.md

@@ -34,35 +34,99 @@ inference:
 # GENIUS: generating text using sketches!
 
 
-```
-from transformers import pipeline
-genius = pipeline("text2text-generation", model="beyond/genius-large", device=0)
-sketch = "<mask> Conference on Empirical Methods <mask> submission of research papers <mask> Deep Learning <mask>"
-genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text']
-```
-
 - **Paper: [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://arxiv.org/abs/2211.10330)**
 - **GitHub: [GENIUS, Pre-training/Data Augmentation Tutorial](https://github.com/beyondguo/genius)**
 
 
 
-💡**GENIUS** is a powerful conditional text generation model using sketches as input, which can fill in the missing contexts for a given **sketch** (key information consisting of textual spans, phrases, or words, concatenated by mask tokens). GENIUS is pre-trained on a large-scale textual corpus with a novel *reconstruction from sketch* objective using an *extreme and selective masking* strategy, enabling it to generate diverse and high-quality texts given sketches.
-
-**GENIUS** can also be used as a general textual **data augmentation tool** for **various NLP tasks** (including sentiment analysis, topic classification, NER, and QA). 
-
-
-![image-20221119164544165](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/hi-genius.png)
-
-
-**Model variations:**
-
-| Model | #params | Language | comment|
-|------------------------|--------------------------------|-------|---------|
-| [`genius-large`](https://huggingface.co/beyond/genius-large) | 406M   | English | The version used in **paper** (recommend) |
-| [`genius-large-k2t`](https://huggingface.co/beyond/genius-large-k2t)  | 406M    | English | keywords-to-text |
-| [`genius-base`](https://huggingface.co/beyond/genius-base)  | 139M    | English | smaller version |
-| [`genius-base-ps`](https://huggingface.co/beyond/genius-base)  | 139M    | English | pre-trained both in paragraphs and short sentences |
-| [`genius-base-chinese`](https://huggingface.co/beyond/genius-base-chinese) | 116M    | 中文 | 在一千万纯净中文段落上预训练|
-
-
+# BERT base model (uncased)
+
+Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://arxiv.org/abs/1810.04805) and first released in
+[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
+between english and English.
+
+Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
+the Hugging Face team.
+
+## Model description
+
+BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
+was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
+publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
+was pretrained with two objectives:
+
+- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
+  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
+  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
+  GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
+  sentence.
+- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
+  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
+  predict if the two sentences were following each other or not.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
+classifier using the features produced by the BERT model as inputs.
+
+## Model variations
+
+BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.  
+Chinese and multilingual uncased and cased versions followed shortly after.  
+Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.  
+Other 24 smaller models are released afterward.  
+
+The detailed release history can be found on the [google-research/bert readme](https://github.com/google-research/bert/blob/master/README.md) on github.
+
+| Model | #params | Language |
+|------------------------|--------------------------------|-------|
+| [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) | 110M   | English |
+| [`bert-large-uncased`](https://huggingface.co/bert-large-uncased)              | 340M    | English | sub 
+| [`bert-base-cased`](https://huggingface.co/bert-base-cased)        | 110M    | English |
+| [`bert-large-cased`](https://huggingface.co/bert-large-cased) | 340M    |  English |
+| [`bert-base-chinese`](https://huggingface.co/bert-base-chinese) | 110M    | Chinese |
+| [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) | 110M | Multiple |
+| [`bert-large-uncased-whole-word-masking`](https://huggingface.co/bert-large-uncased-whole-word-masking) | 340M | English |
+| [`bert-large-cased-whole-word-masking`](https://huggingface.co/bert-large-cased-whole-word-masking) | 340M | English |
+
+## Intended uses & limitations
+
+You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
+be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
+fine-tuned versions of a task that interests you.
+
+Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
+to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
+generation you should look at model like GPT2.
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
+>>> unmasker("Hello I'm a [MASK] model.")
+
+[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
+  'score': 0.1073106899857521,
+  'token': 4827,
+  'token_str': 'fashion'},
+ {'sequence': "[CLS] hello i'm a role model. [SEP]",
+  'score': 0.08774490654468536,
+  'token': 2535,
+  'token_str': 'role'},
+ {'sequence': "[CLS] hello i'm a new model. [SEP]",
+  'score': 0.05338378623127937,
+  'token': 2047,
+  'token_str': 'new'},
+ {'sequence': "[CLS] hello i'm a super model. [SEP]",
+  'score': 0.04667217284440994,
+  'token': 3565,
+  'token_str': 'super'},
+ {'sequence': "[CLS] hello i'm a fine model. [SEP]",
+  'score': 0.027095865458250046,
+  'token': 2986,
+  'token_str': 'fine'}]
+```