Instructions to use j-hartmann/MindMiner-Binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use j-hartmann/MindMiner-Binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="j-hartmann/MindMiner-Binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("j-hartmann/MindMiner-Binary") model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/MindMiner-Binary") - Notebooks
- Google Colab
- Kaggle
Commit ·
938eec9
1
Parent(s): 96ba15c
Update config.json
Browse files- config.json +8 -0
config.json
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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"hidden_act": "gelu",
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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": "low",
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"1": "high"
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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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"low": 0,
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"high": 1
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "roberta",
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