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--- |
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tags: autotrain |
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language: en |
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widget: |
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- text: "I love AutoTrain 🤗" |
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- Output: "Positive" |
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datasets: |
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- Souvikcmsa/autotrain-data-sentiment_analysis |
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co2_eq_emissions: 0.029363397844935534 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-class Classification (3-class Sentiment Classification) |
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## Validation Metrics |
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If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80% of macro and micro F1 score. |
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- Loss: 0.4992932379245758 |
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- Accuracy: 0.799017824663514 |
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- Macro F1: 0.8021508522962549 |
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- Micro F1: 0.799017824663514 |
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- Weighted F1: 0.7993775463659935 |
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- Macro Precision: 0.80406197665167 |
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- Micro Precision: 0.799017824663514 |
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- Weighted Precision: 0.8000374433849405 |
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- Macro Recall: 0.8005261994732908 |
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- Micro Recall: 0.799017824663514 |
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- Weighted Recall: 0.799017824663514 |
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## Usage |
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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/Souvikcmsa/autotrain-sentiment_analysis-762923428 |
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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("Souvikcmsa/autotrain-sentiment_analysis-762923428", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/autotrain-sentiment_analysis-762923428", 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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OR |
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``` |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model = "Souvikcmsa/BERT_sentiment_analysis") |
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classifier("I loved Star Wars so much!")# Positive |
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classifier("A soccer game with multiple males playing. Some men are playing a sport.")# Neutral |
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``` |
