Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
hugging
text-embeddings-inference
Instructions to use ChennabathniArjun/sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChennabathniArjun/sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChennabathniArjun/sentiment_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ChennabathniArjun/sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("ChennabathniArjun/sentiment_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2af74cf74acc0012997208b6cd1fb730d25345f93611e4239c51937a7e676e6a
- Size of remote file:
- 5.71 kB
- SHA256:
- 377256a42d85db35dfc04d4d256a9e84d99d2521cd39a353df8b9f828055ecdc
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