gchhablani commited on
Commit
d613347
1 Parent(s): 0056198
Files changed (2) hide show
  1. README.md +117 -0
  2. config.json +24 -0
README.md ADDED
@@ -0,0 +1,117 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: en
3
+ tags:
4
+ - exbert
5
+ - multiberts
6
+ license: apache-2.0
7
+ datasets:
8
+ - bookcorpus
9
+ - wikipedia
10
+ ---
11
+ # MultiBERTs Seed 400001 Checkpoint 400k (uncased)
12
+ Seed 400001 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
13
+ [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
14
+ [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This model is uncased: it does not make a difference
15
+ between english and English.
16
+
17
+ 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).
18
+
19
+ ## Model description
20
+ MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
21
+ was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
22
+ publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
23
+ was pretrained with two objectives:
24
+ - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
25
+ the entire masked sentence through the model and has to predict the masked words. This is different from traditional
26
+ recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
27
+ GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
28
+ sentence.
29
+ - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
30
+ they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
31
+ predict if the two sentences were following each other or not.
32
+ This way, the model learns an inner representation of the English language that can then be used to extract features
33
+ useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
34
+ classifier using the features produced by the MultiBERTs model as inputs.
35
+
36
+ ## Intended uses & limitations
37
+ You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
38
+ be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for
39
+ fine-tuned versions on a task that interests you.
40
+ Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
41
+ to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
42
+ generation you should look at model like GPT2.
43
+
44
+ ### How to use
45
+ Here is how to use this model to get the features of a given text in PyTorch:
46
+ ```python
47
+ from transformers import BertTokenizer, BertModel
48
+ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
49
+ model = BertModel.from_pretrained("multiberts-seed-400001-400k")
50
+ text = "Replace me by any text you'd like."
51
+ encoded_input = tokenizer(text, return_tensors='pt')
52
+ output = model(**encoded_input)
53
+ ```
54
+
55
+ ### Limitations and bias
56
+ Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
57
+ predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular
58
+ 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.
59
+
60
+ ## Training data
61
+ The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
62
+ unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
63
+ headers).
64
+ ## Training procedure
65
+
66
+ ### Preprocessing
67
+ The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
68
+ then of the form:
69
+ ```
70
+ [CLS] Sentence A [SEP] Sentence B [SEP]
71
+ ```
72
+ With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
73
+ the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
74
+ consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
75
+ "sentences" has a combined length of less than 512 tokens.
76
+ The details of the masking procedure for each sentence are the following:
77
+ - 15% of the tokens are masked.
78
+ - In 80% of the cases, the masked tokens are replaced by `[MASK]`.
79
+ - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
80
+ - In the 10% remaining cases, the masked tokens are left as is.
81
+
82
+ ### Pretraining
83
+ The model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
84
+ of 256. The sequence length was set to 512 throughout. The optimizer
85
+ 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,
86
+ learning rate warmup for 10,000 steps and linear decay of the learning rate after.
87
+
88
+ ### BibTeX entry and citation info
89
+ ```bibtex
90
+ @article{DBLP:journals/corr/abs-2106-16163,
91
+ author = {Thibault Sellam and
92
+ Steve Yadlowsky and
93
+ Jason Wei and
94
+ Naomi Saphra and
95
+ Alexander D'Amour and
96
+ Tal Linzen and
97
+ Jasmijn Bastings and
98
+ Iulia Turc and
99
+ Jacob Eisenstein and
100
+ Dipanjan Das and
101
+ Ian Tenney and
102
+ Ellie Pavlick},
103
+ title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis},
104
+ journal = {CoRR},
105
+ volume = {abs/2106.16163},
106
+ year = {2021},
107
+ url = {https://arxiv.org/abs/2106.16163},
108
+ eprinttype = {arXiv},
109
+ eprint = {2106.16163},
110
+ timestamp = {Mon, 05 Jul 2021 15:15:50 +0200},
111
+ biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib},
112
+ bibsource = {dblp computer science bibliography, https://dblp.org}
113
+ }
114
+ ```
115
+ <a href="https://huggingface.co/exbert/?model=multiberts">
116
+ <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
117
+ </a>
config.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "BertForPreTraining"
4
+ ],
5
+ "attention_probs_dropout_prob": 0.1,
6
+ "classifier_dropout": null,
7
+ "hidden_act": "gelu",
8
+ "hidden_dropout_prob": 0.1,
9
+ "hidden_size": 768,
10
+ "initializer_range": 0.02,
11
+ "intermediate_size": 3072,
12
+ "layer_norm_eps": 1e-12,
13
+ "max_position_embeddings": 512,
14
+ "model_type": "bert",
15
+ "num_attention_heads": 12,
16
+ "num_hidden_layers": 12,
17
+ "pad_token_id": 0,
18
+ "position_embedding_type": "absolute",
19
+ "torch_dtype": "float32",
20
+ "transformers_version": "4.11.0.dev0",
21
+ "type_vocab_size": 2,
22
+ "use_cache": true,
23
+ "vocab_size": 30522
24
+ }