Instructions to use FartLabs/Stable_C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FartLabs/Stable_C with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FartLabs/Stable_C")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FartLabs/Stable_C") model = AutoModelForSequenceClassification.from_pretrained("FartLabs/Stable_C") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +10 -10
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": "
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"1": "
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"2": "
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"3": "
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"4": "
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 514,
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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": "Bitter",
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"1": "Sour",
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"2": "Sweet",
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"3": "Umami",
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"4": "Undefined"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"Bitter": 0,
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"Sour": 1,
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"Sweet": 2,
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"Umami": 3,
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"Undefined": 4
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 514,
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