Model Training Pipeline
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Model Training Pipeline has 7 facts recorded in Dontopedia across 2 references, with 2 live disagreements.
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demonstratesDemonstrates(1)
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describesWorkflowDescribes Workflow(1)
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| Predicate | Value | Ref |
|---|---|---|
| Includes | Data Preparation | [2] |
| Includes | Model Definition | [2] |
| Includes | Model Initialization | [2] |
| Includes | Training Execution | [2] |
| Has Step | Data Preprocessing Section | [1] |
| Has Step | Model Fine Tuning Section | [1] |
| Rdf:type | Workflow | [1] |
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References (2)
ctx:claims/beam/d63b152b-34b0-4323-aea7-f9df40b773a8- full textbeam-chunktext/plain1 KB
doc:beam/d63b152b-34b0-4323-aea7-f9df40b773a8Show excerpt
#### 1. Data Preprocessing ```python from transformers import LlamaTokenizer import torch # Load tokenizer tokenizer = LlamaTokenizer.from_pretrained("llama-2-13b") # Tokenize dataset def tokenize_function(examples): return tokenizer…
ctx:claims/beam/9dc04f5c-41c0-4f03-9508-0f47a466d19e- full textbeam-chunktext/plain1 KB
doc:beam/9dc04f5c-41c0-4f03-9508-0f47a466d19eShow excerpt
#### Dropout Add dropout layers to your model to randomly drop out a fraction of the neurons during training. ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset …
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