init readme

README.md

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+
+---
+language: bn
+tags:
+- collaborative
+- bengali
+- NER
+license: apache-2.0
+datasets: xtreme 
+metrics:
+- Loss
+- Accuracy
+- Precision
+- Recall
+---
+
+# sahajBERT Named Entity Recognition
+
+## Model description
+
+[sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) fine-tuned for NER using the bengali of [WikiANN ](https://huggingface.co/datasets/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:
+```python
+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
+
+<!-- Provide examples of latent issues and potential remediations. -->
+WIP
+
+## Training data
+
+The model was initialized it with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) at step TODO_REPLACE_BY_STEP_NAME and trained on the bengali of [WikiANN ](https://huggingface.co/datasets/wikiann)
+
+## Training procedure
+
+Coming soon! 
+<!-- ```bibtex
+@inproceedings{...,
+  year={2020}
+}
+``` -->
+
+## Eval results
+
+TODO_REPLACE_BY_METRICS
+
+### BibTeX entry and citation info
+
+Coming soon! 
+<!-- ```bibtex
+@inproceedings{...,
+  year={2020}
+}
+``` -->