metadata
language: bn
tags:
- collaborative
- bengali
- SequenceClassification
license: apache-2.0
datasets: IndicGlue
metrics:
- Loss
- Accuracy
- Precision
- Recall
sahajBERT News Article Classification
Model description
sahajBERT fine-tuned for news article classification using the sna.bn split of IndicGlue.
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:
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
WIP
Training data
The model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the sna.bn 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!