MultiBertGunjanPatrick
/

multiberts-seed-0-0k

@@ -8,13 +8,14 @@ datasets:
 - bookcorpus
 - wikipedia
 ---
-# MultiBERTs Seed 0 Checkpoint 0(uncased)
-Seed 0 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
@@ -30,24 +31,27 @@ was pretrained with two objectives:
   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.
 ## 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 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-0")
 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
@@ -58,6 +62,7 @@ The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com
 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:
@@ -73,11 +78,13 @@ The details of the masking procedure for each sentence are the following:
 - 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,

 - bookcorpus
 - wikipedia
 ---
+# MultiBERTs Seed 0 Checkpoint 0k (uncased)
+Seed 0 intermediate checkpoint 0k 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
   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-0-0k")
 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
 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:
 - 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,