Instructions to use dpavlis/distilbert-base-uncased-finetuned-emotions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpavlis/distilbert-base-uncased-finetuned-emotions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpavlis/distilbert-base-uncased-finetuned-emotions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpavlis/distilbert-base-uncased-finetuned-emotions") model = AutoModelForSequenceClassification.from_pretrained("dpavlis/distilbert-base-uncased-finetuned-emotions") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model-index:
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- name: distilbert-base-uncased-finetuned-emotions
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results: []
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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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- Transformers 4.48.3
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- Pytorch 2.6.0
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- Datasets 3.3.2
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- Tokenizers 0.21.0
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model-index:
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- name: distilbert-base-uncased-finetuned-emotions
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results: []
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datasets:
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- jeffnyman/emotions
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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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- Transformers 4.48.3
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- Pytorch 2.6.0
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- Datasets 3.3.2
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- Tokenizers 0.21.0
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