Text Classification
Transformers
ONNX
Safetensors
PyTorch
English
multi-label-classification
multi-class-classification
emotion
bert
go_emotions
emotion-classification
sentiment-analysis
tensorflow
Eval Results (legacy)
Instructions to use logasanjeev/bert-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use logasanjeev/bert-emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="logasanjeev/bert-emotion-classifier")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("logasanjeev/bert-emotion-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update onnx_inference.py
Browse files- onnx_inference.py +1 -1
onnx_inference.py
CHANGED
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@@ -17,7 +17,7 @@ def preprocess_text(text):
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def load_model_and_resources():
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"""Load the ONNX model, tokenizer, emotion labels, and thresholds from Hugging Face."""
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repo_id = "logasanjeev/
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try:
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tokenizer = BertTokenizer.from_pretrained(repo_id)
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def load_model_and_resources():
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"""Load the ONNX model, tokenizer, emotion labels, and thresholds from Hugging Face."""
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repo_id = "logasanjeev/emotions-analyzer-bert"
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try:
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tokenizer = BertTokenizer.from_pretrained(repo_id)
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