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--- |
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language: sv |
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license: mit |
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--- |
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## Swedish BERT models for sentiment analysis, Sentiment targets. |
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[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for target/role assignment in Swedish. The two models are based on the [KB/bert-base-swedish-cased](https://huggingface.co./KB/bert-base-swedish-cased), the models as has been fine tuned to solve a Named Entety Recognition(NER) token classification task. |
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This is a downstream model to be used in conjunction with the [Swedish violence sentiment classifier](https://huggingface.co./RecordedFuture/Swedish-Sentiment-Violence) or [Swedish violence sentiment classifier](https://huggingface.co./RecordedFuture/Swedish-Sentiment-Fear). The models are trained to tag parts of sentences that has recieved a positive classification from the upstream sentiment classifier. The model will tag parts of sentences that contains the targets that the upstream model has activated on. |
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The NER sentiment target models do work as standalone models but their recommended application is downstreamfrom a sentence classification model. |
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The models are only trained on Swedish data and only supports inference of Swedish input texts. The models inference metrics for all non-Swedish inputs are not defined, these inputs are considered as out of domain data. |
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The current models are supported at Transformers version >= 4.3.3 and Torch version 1.8.0, compatibility with older versions are not verified. |
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### Fear targets |
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The model can be imported from the transformers library by running |
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from transformers import BertForSequenceClassification, BertTokenizerFast |
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tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear-Targets") |
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classifier_fear_targets= BertForTokenClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear-Targets") |
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When the model and tokenizer are initialized the model can be used for inference. |
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#### Verification metrics |
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During training the Fear target model had the following verification metrics when using "any overlap" as the evaluation metric. |
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| F-score | Precision | Recall | |
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|:-------------------------:|:-------:|:---------:|:------:| |
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| 0.8361 | 0.7903 | 0.8876 | |
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#### Swedish-Sentiment-Violence |
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The model be can imported from the transformers library by running |
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from transformers import BertForSequenceClassification, BertTokenizerFast |
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tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence-Targets") |
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classifier_violence_targets = BertForTokenClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence-Targets") |
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When the model and tokenizer are initialized the model can be used for inference. |
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#### Verification metrics |
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During training the Violence target model had the following verification metrics when using "any overlap" as the evaluation metric. |
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| F-score | Precision | Recall | |
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|:-------------------------:|:-------:|:---------:|:------:| |
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| 0.7831| 0.9155| 0.8442 | |
