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
Create README.md
Browse files
README.md
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---
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datasets:
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- stanfordnlp/imdb
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language:
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- en
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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base_model:
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- google-bert/bert-base-uncased
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pipeline_tag: text-classification
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library_name: transformers
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tags:
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- bert
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- text-classification
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- sentiment-analysis
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- hugging
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