Text Classification
Transformers
Safetensors
English
bert
emotion-classification
multi-label
goemotions
contrastive-learning
tri-tower
Eval Results (legacy)
Instructions to use sdeakin/fine_tuned_bert_emotions_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sdeakin/fine_tuned_bert_emotions_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sdeakin/fine_tuned_bert_emotions_large")# Load model directly from transformers import AutoTokenizer, MultiLabelBert tokenizer = AutoTokenizer.from_pretrained("sdeakin/fine_tuned_bert_emotions_large") model = MultiLabelBert.from_pretrained("sdeakin/fine_tuned_bert_emotions_large") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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model_name = "
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tok = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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model_name = "sdeakin/fine_tuned_bert_emotions_large"
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tok = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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