Text Classification
Transformers
Safetensors
English
bert
Generated from Trainer
sentiment_analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use cvnberk/crypto_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvnberk/crypto_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cvnberk/crypto_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cvnberk/crypto_sentiment") model = AutoModelForSequenceClassification.from_pretrained("cvnberk/crypto_sentiment") - Notebooks
- Google Colab
- Kaggle
CK commited on
Commit ·
a1f912e
1
Parent(s): c1a819f
Update config.json
Browse files- config.json +3 -3
config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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0: "Negative",
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1: "Neutral",
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2: "Positive"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "Negative",
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"1": "Neutral",
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"2": "Positive"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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