Instructions to use Kate-lf/emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kate-lf/emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kate-lf/emotion-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kate-lf/emotion-classification") model = AutoModelForSequenceClassification.from_pretrained("Kate-lf/emotion-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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library_name: transformers
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license: apache-2.0
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base_model: bert-base-chinese
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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- Transformers 4.57.3
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- Pytorch 2.11.0+cu128
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- Datasets 5.0.0
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- Tokenizers 0.22.2
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---
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library_name: transformers
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license: apache-2.0
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base_model: bert-base-chinese
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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: emotion-classification
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results: []
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language:
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- zh
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pipeline_tag: text-classification
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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- Transformers 4.57.3
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- Pytorch 2.11.0+cu128
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- Datasets 5.0.0
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- Tokenizers 0.22.2
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