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
- Xet hash:
- 4b203f90405d2af6d1aeba6d74d8108da435e30ad3e7c1251656b62c56a1bf80
- Size of remote file:
- 268 MB
- SHA256:
- 7ef710b40e69cc0579fc299a81dcc42d9cc9bb1a179d69b2fe2cd6d2478269ac
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