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---
base_model: ./core-350
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: core-350
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# core-350
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.2048|± |0.0118|
| | |acc_norm|0.2509|± |0.0127|
|arc_easy | 0|acc |0.4247|± |0.0101|
| | |acc_norm|0.3965|± |0.0100|
|boolq | 1|acc |0.5468|± |0.0087|
|hellaswag | 0|acc |0.2844|± |0.0045|
| | |acc_norm|0.3031|± |0.0046|
|openbookqa | 0|acc |0.1560|± |0.0162|
| | |acc_norm|0.2660|± |0.0198|
|piqa | 0|acc |0.5854|± |0.0115|
| | |acc_norm|0.5762|± |0.0115|
|winogrande | 0|acc |0.4909|± |0.0141|
This model is a fine-tuned version of [./core-350](https://huggingface.co/./core-350) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8128
- Accuracy: 0.8237
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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