PyTorch Dataset
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PyTorch Dataset is Custom PyTorch dataset to handle tokenized data and labels.
Mostly:rdf:type(3), handles(2), description(1)
Maturity scale
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inheritsFromInherits From(2)
- Query Dataset
ex:query-dataset - Query Dataset Class
ex:query-dataset-class
classClass(1)
- Dataset
ex:dataset
hasMemberHas Member(1)
- Steps List
ex:steps-list
hasPartHas Part(1)
- Multi Language Tokenization Model
ex:multi-language-tokenization-model
inversePrecedesInverse Precedes(1)
- Steps List
ex:steps-list
precedesPrecedes(1)
- Tokenization Step
ex:tokenization-step
Other facts (10)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Dataset | [1] |
| Rdf:type | Py Torch Class | [2] |
| Rdf:type | Data Structure | [3] |
| Handles | Labels | [1] |
| Handles | Text Data | [1] |
| Description | Custom PyTorch dataset to handle tokenized data and labels | [1] |
| Framework | PyTorch | [1] |
| Precedes | Training Arguments | [1] |
| Inverse Precedes | Training Arguments | [1] |
| Used by | Trainer Class | [3] |
Timeline
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References (3)
ctx:claims/beam/6e640b7d-dae6-4bd7-ab64-9938ce4c792d- full textbeam-chunktext/plain966 B
doc:beam/6e640b7d-dae6-4bd7-ab64-9938ce4c792dShow excerpt
3. **Tokenization**: - Tokenized the text data using the tokenizer from the pre-trained model. 4. **PyTorch Dataset**: - Created a custom PyTorch dataset to handle the tokenized data and labels. 5. **Training Arguments**: - Defin…
ctx:claims/beam/37089ae6-6ce4-42e5-87a2-1cfd71693a4d- full textbeam-chunktext/plain1 KB
doc:beam/37089ae6-6ce4-42e5-87a2-1cfd71693a4dShow excerpt
5. **Parallel Processing**: - Utilize multi-threading or multi-processing for data loading. Here's an optimized version of your code: ### Optimized Code ```python import torch import torch.nn as nn import torch.optim as optim from tor…
ctx:claims/beam/642230b7-a467-4264-a1e9-d36de0c71614- full textbeam-chunktext/plain944 B
doc:beam/642230b7-a467-4264-a1e9-d36de0c71614Show excerpt
3. **Evaluate Accuracy**: Implement a function to evaluate the accuracy of the tokenization against ground truth labels. 4. **Fine-Tuning Example**: Prepare training data, convert it to a PyTorch dataset, and fine-tune the model using the `…
See also
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