Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use racro/sentiment-browser-extension with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use racro/sentiment-browser-extension with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="racro/sentiment-browser-extension")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("racro/sentiment-browser-extension") model = AutoModelForSequenceClassification.from_pretrained("racro/sentiment-browser-extension") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: sentiment-browser-extension
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results: []
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# sentiment-browser-extension
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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## Model description
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license: apache-2.0
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tags:
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- generated_from_trainer
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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: sentiment-browser-extension
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results: []
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# sentiment-browser-extension
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7068
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- Accuracy: 0.8516
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- F1: 0.8690
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## Model description
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