
# sahajBERT News Article Classification

## Model description

[sahajBERT](https://huggingface.co/neuropark/sahajBERT) fine-tuned for news article classification using the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). 

The model is trained for classifying articles into 5 different classes:

| Label id | Label |
|:--------:|:----:|
|0 | kolkata|
|1 | state|
|2 | national|
|3 | sports|
|4 | entertainment|
|5 | international|

## Intended uses & limitations

#### How to use

You can use this model directly with a pipeline for Sequence Classification:
```python
from transformers import AlbertForSequenceClassification, TextClassificationPipeline, PreTrainedTokenizerFast

# Initialize tokenizer
tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NCC")

# Initialize model
model = AlbertForSequenceClassification.from_pretrained("neuropark/sahajBERT-NCC")

# Initialize pipeline
pipeline = TextClassificationPipeline(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 with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT) at step 19519 and trained on the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). 

## Training procedure

Coming soon! 
<!-- ```bibtex
@inproceedings{...,
  year={2020}
}
``` -->

## Eval results


Loss: 0.2477145493030548

Accuracy: 0.926293408929837

Macro F1: 0.9079785326650756

Recall: 0.926293408929837

Weighted F1: 0.9266428029354202

Macro Precision: 0.9109938492260489

Micro Precision: 0.926293408929837

Weighted Precision: 0.9288535478995414

Macro Recall: 0.9069095007692186

Micro Recall: 0.926293408929837

Weighted Recall: 0.926293408929837


### BibTeX entry and citation info

Coming soon! 
<!-- ```bibtex
@inproceedings{...,
  year={2020}
}
``` -->

{"language": "bn", "license": "apache-2.0", "tags": ["collaborative", "bengali", "SequenceClassification"], "datasets": "IndicGlue", "metrics": ["Loss", "Accuracy", "Precision", "Recall"], "widget": [{"text": "\u098f\u09b6\u09bf\u09df\u09be\u09df \u09aa\u09cd\u09b0\u09a5\u09ae \u09a6\u09c3\u09b7\u09cd\u099f\u09bf\u09b9\u09c0\u09a8 \u09ac\u09cd\u09af\u0995\u09cd\u09a4\u09bf\u09b0 \u09ae\u09be\u0989\u09a8\u09cd\u099f \u098f\u09ad\u09be\u09b0\u09c7\u09b8\u09cd\u099f \u099c\u09df|"}]}
transformers pytorch albert text-classification collaborative bengali SequenceClassification bn dataset:IndicGlue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #albert #text-classification #collaborative #bengali #SequenceClassification #bn #dataset-IndicGlue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

sahajBERT News Article Classification
=====================================


Model description
-----------------


sahajBERT fine-tuned for news article classification using the 'URL' split of IndicGlue.


The model is trained for classifying articles into 5 different classes:



Intended uses & limitations
---------------------------


#### How to use


You can use this model directly with a pipeline for Sequence Classification:


#### Limitations and bias


WIP


Training data
-------------


The model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the 'URL' split of IndicGlue.


Training procedure
------------------


Coming soon!


Eval results
------------


Loss: 0.2477145493030548


Accuracy: 0.926293408929837


Macro F1: 0.9079785326650756


Recall: 0.926293408929837


Weighted F1: 0.9266428029354202


Macro Precision: 0.9109938492260489


Micro Precision: 0.926293408929837


Weighted Precision: 0.9288535478995414


Macro Recall: 0.9069095007692186


Micro Recall: 0.926293408929837


Weighted Recall: 0.926293408929837


### BibTeX entry and citation info


Coming soon!



#### How to use


You can use this model directly with a pipeline for Sequence Classification: #### Limitations and bias


WIP


Training data
-------------


The model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the 'URL' split of IndicGlue.


Training procedure
------------------


Coming soon!


Eval results
------------


Loss: 0.2477145493030548


Accuracy: 0.926293408929837


Macro F1: 0.9079785326650756


Recall: 0.926293408929837


Weighted F1: 0.9266428029354202


Macro Precision: 0.9109938492260489


Micro Precision: 0.926293408929837


Weighted Precision: 0.9288535478995414


Macro Recall: 0.9069095007692186


Micro Recall: 0.926293408929837


Weighted Recall: 0.926293408929837 ### BibTeX entry and citation info


Coming soon!
TAGS
#transformers #pytorch #albert #text-classification #collaborative #bengali #SequenceClassification #bn #dataset-IndicGlue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 #### How to use


You can use this model directly with a pipeline for Sequence Classification: #### Limitations and bias


WIP


Training data
-------------


The model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the 'URL' split of IndicGlue.


Training procedure
------------------


Coming soon!


Eval results
------------


Loss: 0.2477145493030548


Accuracy: 0.926293408929837


Macro F1: 0.9079785326650756


Recall: 0.926293408929837


Weighted F1: 0.9266428029354202


Macro Precision: 0.9109938492260489


Micro Precision: 0.926293408929837


Weighted Precision: 0.9288535478995414


Macro Recall: 0.9069095007692186


Micro Recall: 0.926293408929837


Weighted Recall: 0.926293408929837 ### BibTeX entry and citation info


Coming soon!
passage: TAGS
#transformers #pytorch #albert #text-classification #collaborative #bengali #SequenceClassification #bn #dataset-IndicGlue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
#### How to use


You can use this model directly with a pipeline for Sequence Classification:#### Limitations and bias


WIP


Training data
-------------


The model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the 'URL' split of IndicGlue.


Training procedure
------------------


Coming soon!


Eval results
------------


Loss: 0.2477145493030548


Accuracy: 0.926293408929837


Macro F1: 0.9079785326650756


Recall: 0.926293408929837


Weighted F1: 0.9266428029354202


Macro Precision: 0.9109938492260489


Micro Precision: 0.926293408929837


Weighted Precision: 0.9288535478995414


Macro Recall: 0.9069095007692186


Micro Recall: 0.926293408929837


Weighted Recall: 0.926293408929837### BibTeX entry and citation info


Coming soon!