From f58039b868745510339164fc15dc84f5d876b09b Mon Sep 17 00:00:00 2001 From: Raviraja Date: Wed, 19 Aug 2020 00:42:09 +0530 Subject: [PATCH] xlm attn readme added --- README.md | 9 ++++++++ applications/classification/README.md | 7 ++++++ .../sentiment_classification/README.md | 20 ++++++++++++++++++ .../sentiment/codemix_xlm_attn.png | Bin 0 -> 80136 bytes 4 files changed, 36 insertions(+) create mode 100644 assets/images/applications/sentiment/codemix_xlm_attn.png diff --git a/README.md b/README.md index ae6d6ba..43029d2 100644 --- a/README.md +++ b/README.md @@ -305,6 +305,15 @@ To alleviate this problem XLM-RoBERTa model (which has been pre-trained on 100 l An F1 score of `0.59` was achieved. Methods to improve this will be explored later on. +### Day 94: Sentimix with XLM-RoBERTa-LSTM-Attention + +The final output from XLM-RoBERTa model were used as input embeddings to bi-directional LSTM model. An attention layer, which takes the outputs from LSTM layer, produces a weighted representation of the input, which is then passed through a classifier for predicting the sentiment of the sentence. + +![codemix](assets/images/applications/sentiment/codemix_xlm_attn.png) + +An F1 score of `0.64` was achieved. + + ### Day 21: Document Classification with Hierarchical Attention Network A Hierarchical Attention Network (HAN) considers the hierarchical structure of documents (document - sentences - words) and includes an attention mechanism that is able to find the most important words and sentences in a document while taking the context into consideration. diff --git a/applications/classification/README.md b/applications/classification/README.md index 7cac8a1..fc1e927 100644 --- a/applications/classification/README.md +++ b/applications/classification/README.md @@ -54,6 +54,13 @@ To alleviate this problem XLM-RoBERTa model (which has been pre-trained on 100 l An F1 score of `0.59` was achieved. Methods to improve this will be explored later on. + +### Sentimix with XLM-RoBERTa-LSTM-Attention.ipynb + +The final output from XLM-RoBERTa model were used as input embeddings to bi-directional LSTM model. An attention layer, which takes the outputs from LSTM layer, produces a weighted representation of the input, which is then passed through a classifier for predicting the sentiment of the sentence. + +An F1 score of `0.64` was achieved. + # Document Classification ## Document Classification with Hierarchical Attention Network diff --git a/applications/classification/sentiment_classification/README.md b/applications/classification/sentiment_classification/README.md index b9978aa..4116649 100644 --- a/applications/classification/sentiment_classification/README.md +++ b/applications/classification/sentiment_classification/README.md @@ -142,3 +142,23 @@ Following metrics were achieved on test data. - `F1-Score`: 0.59 Methods to improve this will be explored later on. + + +### Sentimix with XLM-RoBERTa-LSTM-Attention.ipynb + +The final output from XLM-RoBERTa model were used as input embeddings to bi-directional LSTM model. An attention layer, which takes the outputs from LSTM layer, produces a weighted representation of the input, which is then passed through a classifier for predicting the sentiment of the sentence. + +![codemix](../../../assets/images/applications/sentiment/codemix_xlm_attn.png) + + +Following metrics were achieved on test data. + +- `Precision`: 0.64 + +- `Recall`: 0.64 + +- `F1-Score`: 0.64 + +#### References + +- [Google transliterated data](https://github.com/keshav22bansal/BAKSA_IITK) \ No newline at end of file diff --git a/assets/images/applications/sentiment/codemix_xlm_attn.png b/assets/images/applications/sentiment/codemix_xlm_attn.png new file mode 100644 index 0000000000000000000000000000000000000000..387724ca2b6bdb242102dc83695e237327cee089 GIT binary patch literal 80136 zcmeFYXH=8j(>Dr;3VJJu9aK;eER-}-6jTyOAqgRYR6tBGqys4+2v$T<0Ra^R#eygn zkfxv_c0jR9Y11iK*U*}3og|D0#7_ndXsb3UAPKD=uMa%JbbX3w6PJ@cEHolRk( zgmGh>$5>fejUy3*7*iMcTAGJ6h41w=e8j)Ng7mNP62L^#bydh|BC@KPqnFkMq zqQO4|#>Wrp$NT4gzErIKm!O{y1Q3`{ zC?p07i$N{`mw{TXQp^?$$a25~-VcuQfg{22d@?bDN|^`4for8aRt(+tYBU&NcW;(ta(VuM@=CWXy|2LqA*D?4zD{9kEd1H*-C z6-Q*0aP^27hL*3EnEoN6<^yj3mUUARtbz=V@baa0&pKs56OVj@@^M$V9!NC}MK_*f#&6pYXXh6e|u)F=p! 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