Commit
•
17e24ad
1
Parent(s):
07c0be3
Add model
Browse files- README.md +3 -3
- config.json +23 -23
README.md
CHANGED
@@ -8,8 +8,8 @@ datasets:
|
|
8 |
- bookcorpus
|
9 |
- wikipedia
|
10 |
---
|
11 |
-
# MultiBERTs Seed
|
12 |
-
Seed
|
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.
|
@@ -46,7 +46,7 @@ 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-
|
50 |
text = "Replace me by any text you'd like."
|
51 |
encoded_input = tokenizer(text, return_tensors='pt')
|
52 |
output = model(**encoded_input)
|
|
|
8 |
- bookcorpus
|
9 |
- wikipedia
|
10 |
---
|
11 |
+
# MultiBERTs Seed 0 Checkpoint 800k (uncased)
|
12 |
+
Seed 0 intermediate checkoint 800k 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.
|
|
|
46 |
```python
|
47 |
from transformers import BertTokenizer, BertModel
|
48 |
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
49 |
+
model = BertModel.from_pretrained("multiberts-seed-1-800k")
|
50 |
text = "Replace me by any text you'd like."
|
51 |
encoded_input = tokenizer(text, return_tensors='pt')
|
52 |
output = model(**encoded_input)
|
config.json
CHANGED
@@ -1,24 +1,24 @@
|
|
1 |
{
|
2 |
-
|
3 |
-
|
4 |
-
|
5 |
-
|
6 |
-
|
7 |
-
|
8 |
-
|
9 |
-
|
10 |
-
|
11 |
-
|
12 |
-
|
13 |
-
|
14 |
-
|
15 |
-
|
16 |
-
|
17 |
-
|
18 |
-
|
19 |
-
|
20 |
-
|
21 |
-
|
22 |
-
|
23 |
-
|
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 |
+
}
|