Create model card for tblard/allocine (#4775)

https://huggingface.co/tblard/tf-allocine

model_cards/tblard/tf-allocine/README.md

+---
+language: french
+---
+
+# tf-allociné
+
+A french sentiment analysis model, based on [CamemBERT](https://camembert-model.fr/), and finetuned on a large-scale dataset scraped from [Allociné.fr](http://www.allocine.fr/) user reviews.
+
+## Results
+
+| Validation Accuracy | Validation F1-Score | Test Accuracy | Test F1-Score |
+|--------------------:| -------------------:| -------------:|--------------:|
+|               97.39 |               97.36 |         97.44 |         97.34 |
+
+The dataset and the evaluation code are available on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert).
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("tblard/tf-allocine")
+model = TFAutoModelForSequenceClassification.from_pretrained("tblard/tf-allocine")
+
+nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+print(nlp("Alad'2 est clairement le meilleur film de l'année 2018.")) # POSITIVE
+print(nlp("Juste whoaaahouuu !")) # POSITIVE
+print(nlp("NUL...A...CHIER ! FIN DE TRANSMISSION.")) # NEGATIVE
+print(nlp("Je m'attendais à mieux de la part de Franck Dubosc !")) # NEGATIVE
+```
+
+## Author
+
+Théophile Blard – :email: theophile.blard@gmail.com
+
+If you use this work (code, model or dataset), please cite as:
+
+> Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert>