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garrettbaber
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twitter-roberta-base-anger-intensity

Text Classification
Transformers
PyTorch
Safetensors
Enawené-Nawé
roberta
text-regression
anger
emotion
emotion intensity
text-embeddings-inference
Model card Files
xet
 Community
garrettbaber commited on
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Update README.md

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  ---
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  tags:
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- - autotrain
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  - text-regression
 
 
 
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  language:
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  - unk
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  widget:
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- - text: "I love AutoTrain"
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  datasets:
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- - garrettbaber/autotrain-data-twitter-roberta-base-anger-intensity2
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  co2_eq_emissions:
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  emissions: 0.030118000944741423
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  ---
 
 
 
 
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  # Model Trained Using AutoTrain
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  You can use cURL to access this model:
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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/garrettbaber/autotrain-twitter-roberta-base-anger-intensity2-72775139028
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  ```
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  Or Python API:
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  ```
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer
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- model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/autotrain-twitter-roberta-base-anger-intensity2-72775139028", use_auth_token=True)
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- tokenizer = AutoTokenizer.from_pretrained("garrettbaber/autotrain-twitter-roberta-base-anger-intensity2-72775139028", use_auth_token=True)
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- inputs = tokenizer("I love AutoTrain", return_tensors="pt")
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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-regression
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+ - anger
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+ - emotion
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+ - emotion intensity
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  language:
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  - unk
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  widget:
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+ - text: I am furious
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  datasets:
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+ - SemEval-2018-Task-1-Text-Regression-Task
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  co2_eq_emissions:
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  emissions: 0.030118000944741423
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  ---
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+ # twitter-roberta-base-anger-intensity
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+ This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m on the SemEval 2018 - Task 1 Affect in Tweets (subtask: El-reg / text regression).
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+
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+ Warning: Hosted inference API produces inaccurate values
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  # Model Trained Using AutoTrain
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  You can use cURL to access this model:
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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 am furious"}' https://api-inference.huggingface.co/models/garrettbaber/twitter-roberta-base-anger-intensity
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  ```
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  Or Python API:
 
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  ```
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer
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+ model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity")
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+ tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-anger-intensity")
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+ inputs = tokenizer("I am furious", return_tensors="pt")
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  outputs = model(**inputs)
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  ```