updating Readme
Browse files
README.md
CHANGED
@@ -13,10 +13,26 @@ Specifically, this model is a *bert-base-multilingual-cased* model that was fine
|
|
13 |
#### How to use
|
14 |
You can use this model with Transformers *pipeline* for masked token prediction.
|
15 |
```python
|
16 |
-
from transformers import pipeline
|
17 |
>>> from transformers import pipeline
|
18 |
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba')
|
19 |
>>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
20 |
```
|
21 |
#### Limitations and bias
|
22 |
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
|
|
|
13 |
#### How to use
|
14 |
You can use this model with Transformers *pipeline* for masked token prediction.
|
15 |
```python
|
|
|
16 |
>>> from transformers import pipeline
|
17 |
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba')
|
18 |
>>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")
|
19 |
+
|
20 |
+
[{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Mary Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.1738305538892746,
|
21 |
+
'token': 12176,
|
22 |
+
'token_str': 'Mary'},
|
23 |
+
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.16382873058319092,
|
24 |
+
'token': 13704,
|
25 |
+
'token_str': 'Queen'},
|
26 |
+
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.13272495567798615,
|
27 |
+
'token': 14382,
|
28 |
+
'token_str': 'ti'},
|
29 |
+
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ King Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.12823280692100525,
|
30 |
+
'token': 11515,
|
31 |
+
'token_str': 'King'},
|
32 |
+
{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Lady Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.07841219753026962,
|
33 |
+
'token': 14005,
|
34 |
+
'token_str': 'Lady'}]
|
35 |
+
|
36 |
```
|
37 |
#### Limitations and bias
|
38 |
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
|