Update README.md

README.md

@@ -8,15 +8,27 @@ license: apache-2.0
 
 metrics:
 - accuracy
-- f1-score
 ---
 
 # camembert-fr-covid-tweet-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 66.0 on the dev ~set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7)~.
-# Fine-tuning hyper-parameters
-- learning_rate = 1e-5
-- batch_size = 32
-- warmup = 600
-- max_seq_length = 128
-- num_train_epochs = 3.0
+This model reaches an accuracy of 66.00% 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:
+- chiffres : this means, the tweet talk about statistics of covid.
+- mesures : this means, the tweet talk about measures take by government  of covid 
+- opinions : this means, the tweet talk about opinion of people like fake new. 
+- symptomes : this means, the tweet talk about symptoms or variant of covid.
+- divers : or other
+ 
+ # Pipelining the Model
+ 
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-classification")
+model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-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': 0.93153977394104}]
+```
+