added bangla-bert-base model card and also modified other model cards (#7071)

* added bangla-bert-base

* Apply suggestions from code review
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

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

+---
+language: bn
+tags:
+- bert
+- bengali
+- bengali-lm
+- bangla
+license: MIT
+datasets:
+- common_crawl
+- wikipedia
+- oscar
+---
+
+
+# Bangla BERT Base
+A long way passed. Here is our **Bangla-Bert**! It is now available in huggingface model hub. 
+
+[Bangla-Bert-Base](https://github.com/sagorbrur/bangla-bert) is a pretrained language model of Bengali language using mask language modeling described in [BERT](https://arxiv.org/abs/1810.04805) and it's github [repository](https://github.com/google-research/bert)
+
+
+
+## Pretrain Corpus Details
+Corpus was downloaded from two main sources:
+
+* Bengali commoncrawl copurs downloaded from [OSCAR](https://oscar-corpus.com/)
+* [Bengali Wikipedia Dump Dataset](https://dumps.wikimedia.org/bnwiki/latest/)
+
+After downloading these corpus, we preprocessed it as a Bert format. which is one sentence per line and an extra newline for new documents. 
+
+```
+sentence 1
+sentence 2
+
+sentence 1
+sentence 2
+
+```
+
+## Building Vocab
+We used [BNLP](https://github.com/sagorbrur/bnlp) package for training bengali sentencepiece model with vocab size 102025. We preprocess the output vocab file as Bert format.
+Our final vocab file availabe at [https://github.com/sagorbrur/bangla-bert](https://github.com/sagorbrur/bangla-bert) and also at [huggingface](https://huggingface.co/sagorsarker/bangla-bert-base) model hub.
+
+## Training Details
+* Bangla-Bert was trained with code provided in Google BERT's github repository (https://github.com/google-research/bert)
+* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)
+* Total Training Steps: 1 Million
+* The model was trained on a single Google Cloud TPU 
+
+## Evaluation Results
+
+After training 1 millions steps here is the evaluation resutls. 
+
+```
+global_step = 1000000
+loss = 2.2406516
+masked_lm_accuracy = 0.60641736
+masked_lm_loss = 2.201459
+next_sentence_accuracy = 0.98625
+next_sentence_loss = 0.040997364
+perplexity = numpy.exp(2.2406516) = 9.393331287442784
+Loss for final step: 2.426227
+
+
+```
+
+**NB: If you use this model for any nlp task please share evaluation results with us. We will add it here.** 
+
+
+## How to Use
+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")
+nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
+for pred in nlp(f"আমি বাংলায় {nlp.tokenizer.mask_token} গাই।"):
+  print(pred)
+
+# {'sequence': '[CLS] আমি বাংলায গান গাই । [SEP]', 'score': 0.13404667377471924, 'token': 2552, 'token_str': 'গান'}
+
+```
+
+
+## Author
+[Sagor Sarker](https://github.com/sagorbrur)
+
+## Acknowledgements
+
+* Thanks to Google [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) for providing the free TPU credits - thank you!
+* Thank to all the people around, who always helping us to build something for Bengali.
+
+## Reference
+* https://github.com/google-research/bert
+
+
+
+
+

model_cards/sagorsarker/codeswitch-hineng-lid-lince/README.md

@@ -3,7 +3,7 @@ language:
 - hi
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching

model_cards/sagorsarker/codeswitch-hineng-ner-lince/README.md

@@ -3,7 +3,7 @@ language:
 - hi
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching

model_cards/sagorsarker/codeswitch-hineng-pos-lince/README.md

@@ -3,7 +3,7 @@ language:
 - hi
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching

model_cards/sagorsarker/codeswitch-nepeng-lid-lince/README.md

@@ -3,7 +3,7 @@ language:
 - ne
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching

model_cards/sagorsarker/codeswitch-spaeng-lid-lince/README.md

@@ -3,7 +3,7 @@ language:
 - es
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching

model_cards/sagorsarker/codeswitch-spaeng-ner-lince/README.md

@@ -3,7 +3,7 @@ language:
 - es
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching

model_cards/sagorsarker/codeswitch-spaeng-pos-lince/README.md

@@ -3,7 +3,7 @@ language:
 - es
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching

model_cards/sagorsarker/codeswitch-spaeng-sentiment-analysis-lince/README.md

@@ -3,7 +3,7 @@ language:
 - es
 - en
 datasets:
-- LinCE
+- lince
 license: "MIT"
 tags:
 - codeswitching