|
--- |
|
|
|
|
|
{} |
|
--- |
|
|
|
# Model Card for Model ID |
|
|
|
This model is developed to tag Names, Organisations and addresses. I have used a data combined fro Conll, ontonotes5, and a custom address dataset that was self made. Cleaned |
|
out the tags. |
|
[\"O\", \"B-ORG\", \"I-ORG\", \"B-PER\", \"I-PER\",'B-addr','I-addr'] |
|
|
|
### Model Description |
|
|
|
- **Developed by:** ctrlbuzz |
|
- **Model type:** Bert |
|
- **Language(s) (NLP):** Named Entity recognition |
|
- **Finetuned from model [optional]:** bert-base-cased |
|
|
|
## Uses |
|
|
|
### Direct Use |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModelForTokenClassification |
|
from transformers import pipeline |
|
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased') |
|
model = AutoModelForTokenClassification.from_pretrained("ctrlbuzz/bert-addresses") |
|
nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
|
example = "While Maria was representing Johnson & Associates at a conference in Spain, she mailed me a letter from her new office at 123 Elm St., Apt. 4B, Springfield, IL.", |
|
|
|
print(nlp(example)) |
|
``` |
|
|
|
|
|
|
|
|