MultiBertGunjanPatrick
/

multiberts-seed-1-1600k

@@ -9,10 +9,11 @@ datasets:
 - bookcorpus
 - wikipedia
 ---
-# MultiBERTs Seed 1 Checkpoint 20k (uncased)
-Seed 1 intermediate checkpoint 20k 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).
@@ -47,7 +48,7 @@ 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-1-20k")
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -81,7 +82,7 @@ The details of the masking procedure for each sentence are the following:
 - 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.

 - bookcorpus
 - wikipedia
 ---
+# MultiBERTs Seed 1 Checkpoint 1600k (uncased)
+Seed 1 intermediate checkpoint 1600k 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 is an intermediate checkpoint.
+The final checkpoint can be found at [multiberts-seed-1](https://hf.co/multberts-seed-1). 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).
 ```python
 from transformers import BertTokenizer, BertModel
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertModel.from_pretrained("multiberts-seed-1-1600k")
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
 - In the 10% remaining cases, the masked tokens are left as is.
 ### Pretraining
+The full 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.