metadata
language: bn
tags:
- collaborative
- bengali
- NER
license: apache-2.0
datasets: xtreme
metrics:
- Loss
- Accuracy
- Precision
- Recall
sahajBERT Named Entity Recognition
Model description
sahajBERT fine-tuned for NER using the bengali of WikiANN .
Named Entities predicted by the model:
| Label id | Label |
|---|---|
| 0 | O |
| 1 | B-PER |
| 2 | I-PER |
| 3 | B-ORG |
| 4 | I-ORG |
| 5 | B-LOC |
| 6 | I-LOC |
Intended uses & limitations
How to use
You can use this model directly with a pipeline for masked language modeling:
from transformers import AlbertForTokenClassification, TokenClassificationPipeline, PreTrainedTokenizerFast
# Initialize tokenizer
tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NER")
# Initialize model
model = AlbertForTokenClassification.from_pretrained("neuropark/sahajBERT-NER")
# Initialize pipeline
pipeline = TokenClassificationPipeline(tokenizer=tokenizer, model=model)
raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me
output = pipeline(raw_text)
Limitations and bias
WIP
Training data
The model was initialized it with pre-trained weights of sahajBERT at step TODO_REPLACE_BY_STEP_NAME and trained on the bengali of WikiANN
Training procedure
Coming soon!
Eval results
accuracy: 0.9756540697674418
f1: 0.9570102589154861
loss: 0.13705264031887054
precision: 0.9518950437317785
recall: 0.962180746561886
BibTeX entry and citation info
Coming soon!