Create README.md

README.md

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+---
+language:
+- fr
+tags:
+- classification
+license: apache-2.0
+metrics:
+- accuracy
+widget:
+- 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'..."
+---
+# camembert-fr-covid-tweet-sentiment-classification
+This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2.
+This model reaches an accuracy of 71% on the dev set.
+In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes:
+-positif
+-negatif
+-neutre 
+ 
+ # Pipelining the Model
+ 
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification")
+model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification")
+nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer)
+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 ''...")
+# Output: [{'label': 'opinions', 'score': 0.831]
+```