Feature Extraction
Transformers
Safetensors
English
mpnet
cybersecurity
classification
fine-tuned
text-embeddings-inference
Instructions to use selfconstruct3d/AttackGroup-MPNET with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selfconstruct3d/AttackGroup-MPNET with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="selfconstruct3d/AttackGroup-MPNET")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET") model = AutoModel.from_pretrained("selfconstruct3d/AttackGroup-MPNET") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -58,6 +58,8 @@ import torch.nn as nn
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch.optim as optim
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import numpy as np
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Load explicitly your fine-tuned MPNet model
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# Load explicitly your tokenizer
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tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET")
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from huggingface_hub import hf_hub_download
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import json
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label_to_groupid_file = hf_hub_download(
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repo_id="selfconstruct3d/AttackGroup-MPNET",
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch.optim as optim
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import numpy as np
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from huggingface_hub import hf_hub_download
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import json
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Load explicitly your fine-tuned MPNet model
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# Load explicitly your tokenizer
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tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/AttackGroup-MPNET")
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label_to_groupid_file = hf_hub_download(
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repo_id="selfconstruct3d/AttackGroup-MPNET",
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