Updated README.md

README.md

@@ -1,16 +1,63 @@
 ---
 tags:
-- autotrain
 - text-classification
+- emotion-analysis
 language:
 - it
 widget:
-- text: "I love AutoTrain 🤗"
+- text: I love AutoTrain 🤗
 datasets:
 - tradicio/autotrain-data-it-emotion-analysis
+- dair-ai/emotion
 co2_eq_emissions:
   emissions: 0.4489187526120041
+license: cc-by-sa-4.0
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
 ---
+# IT-EMOTION-ANALYZER
+
+This is a model for emotion analysis of italian sentences trained on a translated dataset by [Google Translator](https://pypi.org/project/deep-translator/). It maps sentences & paragraphs with 6 emotions which are:
+
+- 0: sadness
+- 1: joy
+- 2: love
+- 3: anger
+- 4: fear
+- 5: surprise
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+
+sentences = ["Questa è una frase triste", "Questa è una frase felice", "Questa è una frase di stupore"]
+
+tokenizer = AutoTokenizer.from_pretrained("aiknowyou/it-emotion-analyzer")
+model = AutoModelForSequenceClassification.from_pretrained("aiknowyou/it-emotion-analyzer")
+
+emotion_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+emotion_analysis(sentences)
+```
+Obtaining the following result:
+```python
+[{'label': '0', 'score': 0.9481984972953796},
+ {'label': '1', 'score': 0.9299975037574768},
+ {'label': '5', 'score': 0.9543816447257996}]
+```
 
 # Model Trained Using AutoTrain
 
@@ -30,27 +77,4 @@ co2_eq_emissions:
 - Weighted Precision: 0.828
 - Macro Recall: 0.828
 - Micro Recall: 0.828
-- Weighted Recall: 0.828
-
-
-## Usage
-
-You can use cURL to access this model:
-
-```
-$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/tradicio/autotrain-it-emotion-analysis-43095109829
-```
-
-Or Python API:
-
-```
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-model = AutoModelForSequenceClassification.from_pretrained("tradicio/autotrain-it-emotion-analysis-43095109829", use_auth_token=True)
-
-tokenizer = AutoTokenizer.from_pretrained("tradicio/autotrain-it-emotion-analysis-43095109829", use_auth_token=True)
-
-inputs = tokenizer("I love AutoTrain", return_tensors="pt")
-
-outputs = model(**inputs)
-```
+- Weighted Recall: 0.828