Text Classification
Transformers
PyTorch
Safetensors
English
roberta
argument-mining
opinion-mining
information-extraction
inference-extraction
Twitter
Instructions to use TomatenMarc/WRAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomatenMarc/WRAP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TomatenMarc/WRAP", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TomatenMarc/WRAP") model = AutoModelForSequenceClassification.from_pretrained("TomatenMarc/WRAP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 65d079a
Update config.json
Browse files- config.json +1 -1
config.json
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"Statement": 1
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"layer_norm_eps": 1e-05,
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"max_position_embeddings":
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"Statement": 1
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 130,
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"model_type": "roberta",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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