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Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/mrm8488/distilroberta-base-finetuned-sentiment/README.md

+# DistilRoBERTa + Sentiment Analysis 😂😢😡😃😯
+
+This in an adaption version of [@omarsar0](https://twitter.com/omarsar0) [tutorial](https://t.co/WMnATW0Hwf?amp=1)
+He explains everything so detailed and provided the dataset. I just changed some parameters and created the ```config.json```file to upload it to [🤗Transformers HUB](https://huggingface.co/) 
+
+
+In this tutorial, he shows how to fine-tune a language model (LM) for **emotion classification** with code adapted from this [tutorial](https://zablo.net/blog/post/custom-classifier-on-bert-model-guide-polemo2-sentiment-analysis/) by MARCIN ZABŁOCKI. 
+
+The emotions covered are:
+ - sadness 😢
+ - joy 😃
+ - love 🥰
+ - anger 😡
+ - fear 😱
+ - surprise 😯
+
+## Details of the language model
+The base model used is [DistilRoBERTa](https://huggingface.co/distilroberta-base)
+
+## Details of the downstream task (Sentence classification) - Dataset 📚
+
+| Dataset split               | # Size | # Sequences |
+| ---------------------- | ----- | ------|
+|Train                   | 1.58M | 20000
+| Validation                | 200 KB |
+| Test                      | 202 KB |
+
+
+## Results after training 🏋️‍♀️🧾
+
+emotion |precision    |recall|  f1-score|   support|
+|-------|-------------|------|----------|----------|
+|sadness| 0.973868  |0.949066  |0.961307|      589|
+|joy   |0.970313  |0.901306  |0.934537|       689|
+|love   |0.743119  |0.925714  |0.824427|       175|    
+|anger  | 0.884615|  0.969349|  0.925046|       261|      
+|fear   |0.951456  |0.875000|  0.911628|       224|      
+|surprise|   0.750000|  0.919355|  0.826087|    62|
+|         | | | | |
+|**accuracy**| | |                  0.924000|      2000|
+|**macro avg**|   0.878895|  0.923298|  0.897172|      2000|
+|**weighted avg**|   0.931355|  0.924000|  0.925620|      2000|
+
+## Model in action 🔨
+
+Fast usage with **pipelines** 🧪
+
+```python
+from transformers import pipeline
+
+nlp_sentiment = pipeline(
+    "sentiment-analysis",
+    model="mrm8488/distilroberta-base-finetuned-sentiment",
+    tokenizer="mrm8488/distilroberta-base-finetuned-sentiment"
+)
+
+text = "i feel i should return to the start of the weekend so my loyal readers can get a feeling for things up to this point"
+
+nlp_sentiment(text)
+# Output: [{'label': 'love', 'score': 0.2183746}]
+```
+
+> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
+
+> Made with <span style="color: #e25555;">&hearts;</span> in Spain