Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use YamenRM/Sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YamenRM/Sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YamenRM/Sentiment_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YamenRM/Sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("YamenRM/Sentiment_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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```
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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model_name = "
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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```
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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model_name = "YamenRM/distilbert-sentiment-classifier"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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