# 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:
- 0 : negatif
- 1 : neutre 
- 2 : positif


# Pipelining the Model
 
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("data354/camembert-fr-covid-tweet-sentiment-classification")
model = AutoModelForSequenceClassification.from_pretrained("data354/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]
```
{"language": ["fr"], "license": "apache-2.0", "tags": ["classification"], "metrics": ["accuracy"], "widget": [{"text": "tchai on est morts. on va se faire vacciner et ils vont contr\u00f4ler comme les marionnettes avec des fils. d'apr\u00e8s les 'ont dit'..."}]}
transformers pytorch camembert text-classification classification fr license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #text-classification #classification #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

# camembert-fr-covid-tweet-sentiment-classification
This model is a fine-tune checkpoint of 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:
- 0 : negatif
- 1 : neutre 
- 2 : positif


# Pipelining the Model
 

# camembert-fr-covid-tweet-sentiment-classification
This model is a fine-tune checkpoint of 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:
- 0 : negatif
- 1 : neutre 
- 2 : positif # Pipelining the Model
TAGS
#transformers #pytorch #camembert #text-classification #classification #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # camembert-fr-covid-tweet-sentiment-classification
This model is a fine-tune checkpoint of 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:
- 0 : negatif
- 1 : neutre 
- 2 : positif # Pipelining the Model
passage: TAGS
#transformers #pytorch #camembert #text-classification #classification #fr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# camembert-fr-covid-tweet-sentiment-classification
This model is a fine-tune checkpoint of 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:
- 0 : negatif
- 1 : neutre 
- 2 : positif# Pipelining the Model