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Fix Auth Token
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model.py
CHANGED
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@@ -21,10 +21,10 @@ models = {
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"desc": "A tool to showcase the full capabilities of text analysis NusaBERT fine-tuning has to offer.",
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"interface": text_analysis_interface,
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"pipe": [
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pipeline(model="LazarusNLP/NusaBERT-base-EmoT",
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pipeline(model="LazarusNLP/NusaBERT-base-EmoT",
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pipeline(model="LazarusNLP/NusaBERT-base-POSP",
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pipeline(model="LazarusNLP/NusaBERT-base-NERP",
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],
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},
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"Sentiment Analysis": {
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@@ -37,7 +37,7 @@ models = {
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"output_label": "Sentiment Analysis",
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"desc": "A sentiment-text-classification model based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's SmSA dataset consisting of Indonesian comments and reviews.",
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"interface": text_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-EmoT",
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},
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"Emotion Detection": {
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"title": "Emotion Classifier",
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@@ -49,7 +49,7 @@ models = {
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"output_label": "Emotion Classifier",
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"desc": "An emotion classifier based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's EmoT dataset",
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"interface": text_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-EmoT",
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},
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"POS Tagging": {
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"title": "POS Tagging",
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@@ -61,7 +61,7 @@ models = {
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"output_label": "POS Tagging",
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"desc": "A part-of-speech token-classification model based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's POSP dataset consisting of tag-labelled news.",
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"interface": token_classification_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-POSP",
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},
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"NER Tagging": {
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"title": "NER Tagging",
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@@ -73,6 +73,6 @@ models = {
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"output_label": "NER Tagging",
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"desc": "A NER Tagging token-classification model based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's NERP dataset consisting of tag-labelled news.",
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"interface": token_classification_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-NERP",
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},
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}
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"desc": "A tool to showcase the full capabilities of text analysis NusaBERT fine-tuning has to offer.",
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"interface": text_analysis_interface,
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"pipe": [
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pipeline(model="LazarusNLP/NusaBERT-base-EmoT", token=auth_token),
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pipeline(model="LazarusNLP/NusaBERT-base-EmoT", token=auth_token),
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pipeline(model="LazarusNLP/NusaBERT-base-POSP", token=auth_token),
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pipeline(model="LazarusNLP/NusaBERT-base-NERP", token=auth_token),
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],
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},
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"Sentiment Analysis": {
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"output_label": "Sentiment Analysis",
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"desc": "A sentiment-text-classification model based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's SmSA dataset consisting of Indonesian comments and reviews.",
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"interface": text_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-EmoT", token=auth_token),
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},
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"Emotion Detection": {
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"title": "Emotion Classifier",
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"output_label": "Emotion Classifier",
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"desc": "An emotion classifier based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's EmoT dataset",
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"interface": text_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-EmoT", token=auth_token),
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},
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"POS Tagging": {
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"title": "POS Tagging",
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"output_label": "POS Tagging",
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"desc": "A part-of-speech token-classification model based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's POSP dataset consisting of tag-labelled news.",
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"interface": token_classification_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-POSP", token=auth_token),
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},
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"NER Tagging": {
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"title": "NER Tagging",
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"output_label": "NER Tagging",
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"desc": "A NER Tagging token-classification model based on the BERT model. The model was originally the pre-trained NusaBERT Base model, which is then fine-tuned on indonlu's NERP dataset consisting of tag-labelled news.",
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"interface": token_classification_interface,
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"pipe": pipeline(model="LazarusNLP/NusaBERT-base-NERP", token=auth_token),
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},
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}
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