Add model

README.md

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+---
+language: en
+tags:
+- exbert
+- multiberts
+license: apache-2.0
+datasets:
+- bookcorpus
+- wikipedia
+---
+# MultiBERTs Seed 80000 Checkpoint 80k (uncased)
+Seed 80000 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
+[this repository](https://github.com/google-research/language/tree/master/language/multiberts). This model is uncased: it does not make a difference
+between english and English.
+
+Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
+
+## Model description
+MultiBERTs models are 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 labelling 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 mask 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 MultiBERTs model as inputs.
+
+## 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=multiberts) to look for
+fine-tuned versions on 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
+Here is how to use this model to get the features of a given text in PyTorch:
+```python
+from transformers import BertTokenizer, BertModel
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertModel.from_pretrained("multiberts-seed-80000-80k")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+### Limitations and bias
+Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
+predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular
+checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint.
+
+## Training data
+The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
+unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
+headers).
+## Training procedure
+
+### Preprocessing
+The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
+then of the form:
+```
+[CLS] Sentence A [SEP] Sentence B [SEP]
+```
+With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
+the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
+consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
+"sentences" has a combined length of less than 512 tokens.
+The details of the masking procedure for each sentence are the following:
+- 15% of the tokens are masked.
+- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
+- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
+- In the 10% remaining cases, the masked tokens are left as is.
+
+### Pretraining
+The model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
+of 256. The sequence length was set to 512 throughout. The optimizer
+used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
+learning rate warmup for 10,000 steps and linear decay of the learning rate after.
+
+### BibTeX entry and citation info
+```bibtex
+@article{DBLP:journals/corr/abs-2106-16163,
+  author    = {Thibault Sellam and
+               Steve Yadlowsky and
+               Jason Wei and
+               Naomi Saphra and
+               Alexander D'Amour and
+               Tal Linzen and
+               Jasmijn Bastings and
+               Iulia Turc and
+               Jacob Eisenstein and
+               Dipanjan Das and
+               Ian Tenney and
+               Ellie Pavlick},
+  title     = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis},
+  journal   = {CoRR},
+  volume    = {abs/2106.16163},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2106.16163},
+  eprinttype = {arXiv},
+  eprint    = {2106.16163},
+  timestamp = {Mon, 05 Jul 2021 15:15:50 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+<a href="https://huggingface.co/exbert/?model=multiberts">
+	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
+</a>

config.json

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+{
+  "architectures": [
+    "BertForPreTraining"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.11.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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