Text Classification
Transformers
PyTorch
Italian
bert
emotion-analysis
Inference Endpoints
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Updated README.md

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  ---
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  tags:
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- - autotrain
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  - text-classification
 
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  language:
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  - it
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  widget:
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- - text: "I love AutoTrain 🤗"
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  datasets:
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  - tradicio/autotrain-data-it-emotion-analysis
 
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  co2_eq_emissions:
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  emissions: 0.4489187526120041
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Model Trained Using AutoTrain
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@@ -30,27 +77,4 @@ co2_eq_emissions:
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  - Weighted Precision: 0.828
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  - Macro Recall: 0.828
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  - Micro Recall: 0.828
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- - Weighted Recall: 0.828
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-
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-
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- ## Usage
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-
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- You can use cURL to access this model:
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-
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- ```
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- $ 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
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- ```
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-
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- Or Python API:
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-
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- ```
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- from transformers import AutoModelForSequenceClassification, AutoTokenizer
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-
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- model = AutoModelForSequenceClassification.from_pretrained("tradicio/autotrain-it-emotion-analysis-43095109829", use_auth_token=True)
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-
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- tokenizer = AutoTokenizer.from_pretrained("tradicio/autotrain-it-emotion-analysis-43095109829", use_auth_token=True)
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-
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- inputs = tokenizer("I love AutoTrain", return_tensors="pt")
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-
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- outputs = model(**inputs)
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- ```
 
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  ---
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  tags:
 
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  - text-classification
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+ - emotion-analysis
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  language:
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  - it
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  widget:
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+ - text: I love AutoTrain 🤗
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  datasets:
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  - tradicio/autotrain-data-it-emotion-analysis
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+ - dair-ai/emotion
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  co2_eq_emissions:
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  emissions: 0.4489187526120041
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+ license: cc-by-sa-4.0
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+ metrics:
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+ - accuracy
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+ - f1
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+ pipeline_tag: text-classification
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  ---
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+ # IT-EMOTION-ANALYZER
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+
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+ 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:
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+
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+ - 0: sadness
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+ - 1: joy
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+ - 2: love
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+ - 3: anger
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+ - 4: fear
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+ - 5: surprise
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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage (Sentence-Transformers)
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+
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ from transformers import pipeline
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+
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+ sentences = ["Questa è una frase triste", "Questa è una frase felice", "Questa è una frase di stupore"]
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+
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+ tokenizer = AutoTokenizer.from_pretrained("aiknowyou/it-emotion-analyzer")
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+ model = AutoModelForSequenceClassification.from_pretrained("aiknowyou/it-emotion-analyzer")
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+
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+ emotion_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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+ emotion_analysis(sentences)
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+ ```
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+ Obtaining the following result:
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+ ```python
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+ [{'label': '0', 'score': 0.9481984972953796},
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+ {'label': '1', 'score': 0.9299975037574768},
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+ {'label': '5', 'score': 0.9543816447257996}]
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+ ```
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  # Model Trained Using AutoTrain
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  - Weighted Precision: 0.828
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  - Macro Recall: 0.828
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  - Micro Recall: 0.828
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+ - Weighted Recall: 0.828