gchhablani commited on
Commit
15868f7
1 Parent(s): 5b2e0d2

Fix README

Browse files
Files changed (1) hide show
  1. README.md +7 -6
README.md CHANGED
@@ -3,16 +3,17 @@ language: en
3
  tags:
4
  - exbert
5
  - multiberts
6
- - multiberts-seed-1
7
  license: apache-2.0
8
  datasets:
9
  - bookcorpus
10
  - wikipedia
11
  ---
12
- # MultiBERTs Seed 1 Checkpoint 20k (uncased)
13
- Seed 1 intermediate checkpoint 20k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
14
  [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
15
- [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This model is uncased: it does not make a difference
 
16
  between english and English.
17
 
18
  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:
47
  ```python
48
  from transformers import BertTokenizer, BertModel
49
  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
50
- model = BertModel.from_pretrained("multiberts-seed-1-20k")
51
  text = "Replace me by any text you'd like."
52
  encoded_input = tokenizer(text, return_tensors='pt')
53
  output = model(**encoded_input)
@@ -81,7 +82,7 @@ The details of the masking procedure for each sentence are the following:
81
  - In the 10% remaining cases, the masked tokens are left as is.
82
 
83
  ### Pretraining
84
- The model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
85
  of 256. The sequence length was set to 512 throughout. The optimizer
86
  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,
87
  learning rate warmup for 10,000 steps and linear decay of the learning rate after.
 
3
  tags:
4
  - exbert
5
  - multiberts
6
+ - multiberts-seed-0
7
  license: apache-2.0
8
  datasets:
9
  - bookcorpus
10
  - wikipedia
11
  ---
12
+ # MultiBERTs Seed 0 Checkpoint 1200k (uncased)
13
+ Seed 0 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
14
  [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
15
+ [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint.
16
+ The final checkpoint can be found at [multiberts-seed-0](https://hf.co/multberts-seed-0). This model is uncased: it does not make a difference
17
  between english and English.
18
 
19
  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).
 
48
  ```python
49
  from transformers import BertTokenizer, BertModel
50
  tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
51
+ model = BertModel.from_pretrained("multiberts-seed-0-1200k")
52
  text = "Replace me by any text you'd like."
53
  encoded_input = tokenizer(text, return_tensors='pt')
54
  output = model(**encoded_input)
 
82
  - In the 10% remaining cases, the masked tokens are left as is.
83
 
84
  ### Pretraining
85
+ The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
86
  of 256. The sequence length was set to 512 throughout. The optimizer
87
  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,
88
  learning rate warmup for 10,000 steps and linear decay of the learning rate after.