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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- text-classification
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- sentiment-analysis
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- distilbert
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- imdb
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- pytorch
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pipeline_tag: text-classification
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datasets:
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- imdb
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metrics:
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- accuracy
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- f1
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model-index:
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- name: ohanvi-sentiment-analysis
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---
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# 🎬 Ohanvi Sentiment Analysis
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classifier = pipeline(
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"text-classification",
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model="
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)
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result = classifier("This movie was absolutely fantastic!")
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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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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model.eval()
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title = {Ohanvi Sentiment Analysis},
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author = {Gourav Bansal},
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year = {2026},
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url = {https://huggingface.co/
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}
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```
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---
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language:
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+
- en
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license: apache-2.0
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library_name: transformers
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tags:
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- text-classification
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- sentiment-analysis
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- distilbert
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- imdb
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- pytorch
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pipeline_tag: text-classification
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datasets:
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- imdb
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metrics:
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- accuracy
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- f1
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model-index:
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- name: ohanvi-sentiment-analysis
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results:
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- task:
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type: text-classification
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name: Sentiment Analysis
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dataset:
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name: IMDb
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type: imdb
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split: test
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metrics:
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- type: accuracy
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value: 0.932
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name: Accuracy
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- type: f1
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value: 0.931
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name: F1
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---
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# 🎬 Ohanvi Sentiment Analysis
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classifier = pipeline(
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"text-classification",
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model="ohanvi/ohanvi-sentiment-analysis",
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)
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result = classifier("This movie was absolutely fantastic!")
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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model_name = "ohanvi/ohanvi-sentiment-analysis"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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model.eval()
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title = {Ohanvi Sentiment Analysis},
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author = {Gourav Bansal},
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year = {2026},
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url = {https://huggingface.co/ohanvi/ohanvi-sentiment-analysis},
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}
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```
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