Instructions to use xshubhamx/bart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/bart-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/bart-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/bart-base") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/bart-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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base_model: facebook/bart-base
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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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- precision
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- recall
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model-index:
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- name: bart-base
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results: []
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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- Accuracy: 0.
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- Precision: 0.
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- Recall: 0.
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- Precision Macro: 0.7608
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- Recall Macro: 0.7799
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- Macro Fpr: 0.0157
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---
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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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- precision
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- recall
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base_model: facebook/bart-base
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model-index:
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- name: bart-base
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results: []
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.7146
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- Accuracy: 0.8180
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- Precision: 0.8189
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- Recall: 0.8180
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- Precision Macro: 0.7608
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- Recall Macro: 0.7799
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- Macro Fpr: 0.0157
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