added evaluation results for classification task (#7790)

model_cards/sagorsarker/bangla-bert-base/README.md

@@ -49,6 +49,7 @@ Our final vocab file availabe at [https://github.com/sagorbrur/bangla-bert](http
 
 ## Evaluation Results
 
+### LM Evaluation Results
 After training 1 millions steps here is the evaluation resutls. 
 
 ```
@@ -61,9 +62,22 @@ next_sentence_loss = 0.040997364
 perplexity = numpy.exp(2.2406516) = 9.393331287442784
 Loss for final step: 2.426227
 
-
 ```
 
+### Downstream Task Evaluation Results
+Huge Thanks to [Nick Doiron](https://twitter.com/mapmeld) for providing evalution results of classification task.
+He used [Bengali Classification Benchmark](https://github.com/rezacsedu/Classification_Benchmarks_Benglai_NLP) datasets for classification task.
+Comparing to Nick's [Bengali electra](https://huggingface.co/monsoon-nlp/bangla-electra) and multi-lingual BERT, Bangla BERT Base achieves state of the art result.
+Here is the [evaluation script](https://github.com/sagorbrur/bangla-bert/blob/master/notebook/bangla-bert-evaluation-classification-task.ipynb).
+
+
+| Model | Sentiment Analysis | Hate Speech Task | News Topic Task | Average |
+| ----- | -------------------| ---------------- | --------------- | ------- |
+| mBERT | 68.15 | 52.32 | 72.27 | 64.25 |
+| Bengali Electra | 69.19 | 44.84 | 82.33 | 65.45 |
+| Bangla BERT Base | 70.37 | 71.83 | 89.19 | 77.13 |
+
+
 **NB: If you use this model for any nlp task please share evaluation results with us. We will add it here.** 
 
 
@@ -73,8 +87,8 @@ You can use this model directly with a pipeline for masked language modeling:
 ```py
 from transformers import BertForMaskedLM, BertTokenizer, pipeline
 
-model = BertForMaskedLM.from_pretrained("bangla-bert-base")
-tokenizer = BertTokenizer.from_pretrained("bangla-bert-base")
+model = BertForMaskedLM.from_pretrained("sagorsarker/bangla-bert-base")
+tokenizer = BertTokenizer.from_pretrained("sagorsarker/bangla-bert-base")
 nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
 for pred in nlp(f"আমি বাংলায় {nlp.tokenizer.mask_token} গাই।"):
   print(pred)