Instructions to use junaid1993/Belief_BERT_MultiClass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junaid1993/Belief_BERT_MultiClass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="junaid1993/Belief_BERT_MultiClass")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("junaid1993/Belief_BERT_MultiClass") model = AutoModelForSequenceClassification.from_pretrained("junaid1993/Belief_BERT_MultiClass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
a955ad8
1
Parent(s): 198b59d
Updated config.json in main and checkpoint directories to ensure label consistency
Browse files
checkpoint-500/config.json
CHANGED
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@@ -9,17 +9,17 @@
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "Strong
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"1": "Moderate
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"2": "Weak
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"3": "No
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},
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"initializer_range": 0.02,
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"label2id": {
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"
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"
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"
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"
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "Strong",
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"1": "Moderate",
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"2": "Weak",
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"3": "No Beliefs Expressed"
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},
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"initializer_range": 0.02,
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"label2id": {
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"Strong": 0,
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"Moderate": 1,
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"Weak": 2,
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"No Beliefs Expressed": 3
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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