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README.md
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@@ -96,7 +96,7 @@ The model was pre-trained continuously on a single A10G GPU in an AWS instance f
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<br>Thus, hurts the performance of the Abstractive Summarization task.
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<br>This case is not present in the decoder-only model as all the predicted next token is not seen by the model at all.
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2. We have pre-trained our model with approx 16 GB of data, and testing Classification result on <a href='https://www.kaggle.com/datasets/ashokpant/nepali-news-dataset-large/data'>Nepali News Dataset (Large)</a> with a couple of transformer based Models available on Hugging Face,
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<br> Our models seem to do better than others with an accuracy of 0.58 on validation but,
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<br> It's seen that we still do not have enough data for generalization as Transformer models only perform well on large amounts of pre-trained data compared with Classical Sequential Models.
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<br>Thus, hurts the performance of the Abstractive Summarization task.
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| 97 |
<br>This case is not present in the decoder-only model as all the predicted next token is not seen by the model at all.
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| 98 |
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| 99 |
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2. We have pre-trained our model with approx 16 GB of data, and testing Classification result on <a href='https://www.kaggle.com/datasets/ashokpant/nepali-news-dataset-large/data'>Nepali News Dataset (Large)</a> with a couple of Nepali transformer based Models available on Hugging Face,
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| 100 |
<br> Our models seem to do better than others with an accuracy of 0.58 on validation but,
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| 101 |
<br> It's seen that we still do not have enough data for generalization as Transformer models only perform well on large amounts of pre-trained data compared with Classical Sequential Models.
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| 102 |
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