ohanvi commited on
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Update model card

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  1. README.md +28 -28
README.md CHANGED
@@ -1,37 +1,37 @@
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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
@@ -67,7 +67,7 @@ from transformers import pipeline
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  classifier = pipeline(
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  "text-classification",
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- model="your-hf-username/ohanvi-sentiment-analysis",
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  )
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  result = classifier("This movie was absolutely fantastic!")
@@ -109,7 +109,7 @@ The model was fine-tuned on the full [IMDb](https://huggingface.co/datasets/imdb
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  import torch
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- model_name = "your-hf-username/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()
@@ -143,7 +143,7 @@ If you use this model, please cite:
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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/your-hf-username/ohanvi-sentiment-analysis},
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  }
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  ```
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  ---
2
  language:
3
+ - en
4
  license: apache-2.0
5
  library_name: transformers
6
  tags:
7
+ - text-classification
8
+ - sentiment-analysis
9
+ - distilbert
10
+ - imdb
11
+ - pytorch
12
  pipeline_tag: text-classification
13
  datasets:
14
+ - imdb
15
  metrics:
16
+ - accuracy
17
+ - f1
18
  model-index:
19
+ - 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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