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--- |
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language: |
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- fr |
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tags: |
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- classification |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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widget: |
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- text: "tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les 'ont dit'..." |
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--- |
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# camembert-fr-covid-tweet-classification |
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This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2. |
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This model reaches an accuracy of 66.00% on the dev set. |
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In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes: |
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- chiffres : this means, the tweet talk about statistics of covid. |
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- mesures : this means, the tweet talk about measures take by government of covid |
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- opinions : this means, the tweet talk about opinion of people like fake new. |
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- symptomes : this means, the tweet talk about symptoms or variant of covid. |
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- divers : or other |
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# Pipelining the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-classification") |
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model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-classification") |
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nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer) |
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nlp_topic_classif("tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les '' ont dit ''...") |
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# Output: [{'label': 'opinions', 'score': 0.831] |
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``` |
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