Instructions to use veriga/tf_disilbert_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use veriga/tf_disilbert_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="veriga/tf_disilbert_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("veriga/tf_disilbert_binary") model = AutoModelForSequenceClassification.from_pretrained("veriga/tf_disilbert_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload TFBertForSequenceClassification
Browse files- README.md +0 -0
- config.json +0 -6
- tf_model.h5 +2 -2
README.md
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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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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 1024,
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"model_type": "bert",
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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-12,
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"max_position_embeddings": 1024,
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"model_type": "bert",
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tf_model.h5
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