File size: 1,484 Bytes
38fff5a 3a393c3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
inference: False
license: apache-2.0
datasets:
- harem
language:
- pt
metrics:
- f1
pipeline_tag: token-classification
---
# Portuguese NER BERT-CRF HAREM Default
This model is a fine-tuned BERT model adapted for Named Entity Recognition (NER) tasks. It utilizes Conditional Random Fields (CRF) as the decoder.
The model follows the HAREM Default labeling scheme for NER. Additionally, it provides options for HAREM Selective and Conll-2003 labeling schemes.
## How to Use
You can employ this model using the Transformers library's *pipeline* for NER, or incorporate it as a conventional Transformer in the HuggingFace ecosystem.
```python
from transformers import pipeline
import torch
import nltk
ner_classifier = pipeline(
"ner",
model="arubenruben/NER-PT-BERT-CRF-HAREM-Default",
device=torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu"),
trust_remote_code=True
)
text = "FCPorto vence o Benfica por 5-0 no Estádio do Dragão"
tokens = nltk.wordpunct_tokenize(text)
result = ner_classifier(tokens)
```
## Demo
There is a [Notebook](https://github.com/arubenruben/PT-Pump-Up/blob/master/BERT-CRF.ipynb) available to test our code.
## PT-Pump-Up
This model is integrated in the project [PT-Pump-Up](https://github.com/arubenruben/PT-Pump-Up)
## Evaluation
#### Testing Data
The model was tested on the Miniharem Testset.
### Results
F1-Score: 0.787
## Citation
Citation will be made available soon.
**BibTeX:**
:( |