Evaluation Strategy
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Evaluation Strategy has 13 facts recorded in Dontopedia across 5 references, with 1 live disagreement.
Mostly:rdf:type(2), parameter value(1), determines frequency(1)
Maturity scale
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Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
describesDescribes(1)
- Coverage Section
ex:coverage-section
has-parameterHas Parameter(1)
- Training Arguments
ex:training-arguments
includesIncludes(1)
- Hyperparameter Tuning
ex:hyperparameter-tuning
instanceOfInstance of(1)
- Continuous Evaluation
ex:continuous-evaluation
specifiesSpecifies(1)
- Training Configuration
ex:training-configuration
Other facts (12)
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 | Evaluation Strategy | [2] |
| Rdf:type | Hyperparameter | [4] |
| Parameter Value | steps | [1] |
| Determines Frequency | Eval Steps | [1] |
| Is Set to | Steps Strategy | [1] |
| Has Value | epoch | [2] |
| Method | Coverage Based Testing | [3] |
| Has Parameter Name | evaluation_strategy | [4] |
| Has Suggested Value | epoch | [4] |
| Has Type | Evaluation Hyperparameter | [4] |
| Has Value | epoch | [4] |
| Has Instance | Continuous Evaluation | [5] |
Timeline
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References (5)
ctx:claims/beam/9500e1c6-ed0c-41a2-ace0-794604c62109- full textbeam-chunktext/plain1 KB
doc:beam/9500e1c6-ed0c-41a2-ace0-794604c62109Show excerpt
- **Strategy**: Use `True` if your hardware supports it (e.g., NVIDIA GPUs with Tensor Cores). ### Example Configuration Here's an example configuration for fine-tuning Llama 2 13B: ```python from transformers import LlamaForCausalLM…
ctx:claims/beam/018e6829-a4ce-4a26-9be8-6d8ad3231779- full textbeam-chunktext/plain1 KB
doc:beam/018e6829-a4ce-4a26-9be8-6d8ad3231779Show excerpt
# Define training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, loggi…
ctx:claims/beam/2a449008-33cb-4087-82ce-ebb7ed137c33- full textbeam-chunktext/plain1 KB
doc:beam/2a449008-33cb-4087-82ce-ebb7ed137c33Show excerpt
2. **Expected Outcomes**: - For each query, define the expected resized query or the expected outcome based on the resizing algorithm. 3. **Coverage**: - Ensure that your test data covers a wide range of complexities and scenarios to…
ctx:claims/beam/1714914a-4272-4b7c-91df-6c89df9429f8- full textbeam-chunktext/plain1 KB
doc:beam/1714914a-4272-4b7c-91df-6c89df9429f8Show excerpt
- **Reason**: More epochs can lead to overfitting, but fewer epochs might not be enough for the model to learn the data well. 2. **Batch Size (`per_device_train_batch_size` and `per_device_eval_batch_size`)**: - **Suggested Value**: …
ctx:claims/beam/6ce64119-b49e-49b8-8f91-06ba5ce02df5
See also
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