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Fix README
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README.md
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@@ -3,16 +3,17 @@ language: en
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tags:
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- exbert
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- multiberts
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- multiberts-seed-
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license: apache-2.0
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datasets:
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- bookcorpus
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- wikipedia
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---
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# MultiBERTs Seed
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Seed
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[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
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[this repository](https://github.com/google-research/language/tree/master/language/multiberts). This
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between english and English.
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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).
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@@ -47,7 +48,7 @@ Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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model = BertModel.from_pretrained("multiberts-seed-
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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@@ -81,7 +82,7 @@ The details of the masking procedure for each sentence are the following:
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- In the 10% remaining cases, the masked tokens are left as is.
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### Pretraining
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The model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
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of 256. The sequence length was set to 512 throughout. The optimizer
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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,
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learning rate warmup for 10,000 steps and linear decay of the learning rate after.
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tags:
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- exbert
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- multiberts
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- multiberts-seed-4
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license: apache-2.0
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datasets:
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- bookcorpus
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- wikipedia
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---
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# MultiBERTs Seed 4 Checkpoint 40k (uncased)
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Seed 4 intermediate checkpoint 40k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
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[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
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[this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint.
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The final checkpoint can be found at [multiberts-seed-4](https://hf.co/multberts-seed-4). This model is uncased: it does not make a difference
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between english and English.
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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).
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```python
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from transformers import BertTokenizer, BertModel
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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model = BertModel.from_pretrained("multiberts-seed-4-40k")
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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- In the 10% remaining cases, the masked tokens are left as is.
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### Pretraining
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The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
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of 256. The sequence length was set to 512 throughout. The optimizer
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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,
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learning rate warmup for 10,000 steps and linear decay of the learning rate after.
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